Tag: Claude

  • Claude Skills vs MCP vs Connectors vs Plugins (2026)

    Claude Skills vs MCP vs Connectors vs Plugins (2026)

    Last verified: June 13, 2026

    The simplest way to keep these straight: Skills teach Claude how to do a task, MCP servers and Connectors give Claude access to external systems, Plugins bundle several of these together, and Hooks and slash commands control a Claude Code session. A Skill is a folder of instructions Claude reads when relevant. MCP (the Model Context Protocol) is an open standard that connects Claude to your tools and data. A Connector is Anthropic’s packaging of a remote MCP server inside the Claude apps. A Plugin packages any combination of commands, agents, MCP servers, hooks, and skills for Claude Code. Every definition below is taken verbatim from Anthropic’s official documentation, fetched on the verification date.

    The one-glance comparison

    Four comparison cards for Claude skills, MCP, connectors, and plugins
    One-glance comparison of the four extension surfaces.

    This is the liftable summary. Each row is one mechanism; the third column is the distinction people most often get wrong — whether the thing teaches Claude how to do something or gives Claude access to something.

    TypeWhat it isTeaches-HOW or gives-ACCESSWhere it runsHow you install / enable it
    Skill A modular capability that packages instructions, metadata, and optional resources (scripts, templates) in a SKILL.md file that Claude uses automatically when relevant. Teaches HOW. Provides domain-specific expertise: workflows, context, and best practices (procedural knowledge). In Claude’s code execution environment / VM, where Claude has filesystem access, bash, and code execution. claude.ai: upload a zip under Settings > Features. API: upload via the Skills API (/v1/skills) with the required beta headers (code-execution-2025-08-25, skills-2025-10-02, files-api-2025-04-14). Claude Code: a SKILL.md directory under ~/.claude/skills/ or .claude/skills/.
    MCP server An implementation of the Model Context Protocol — “an open-source standard for connecting AI applications to external systems.” Described as “a USB-C port for AI applications.” Gives ACCESS. Connects Claude to data sources, tools, and workflows so it can access information and perform tasks. Local (stdio) servers run as processes on your machine; remote servers run over HTTP (recommended) or SSE (deprecated). In Claude Code: claude mcp add, at local, project, or user scope. Also configurable in .mcp.json or imported from Claude Desktop / claude.ai.
    Connector A feature that “let[s] Claude access your apps and services, retrieve your data, and take actions within connected services.” Custom connectors use remote MCP. Gives ACCESS. Same access role as MCP — a Connector is the in-app packaging of a remote MCP server. Custom connectors are reached from Anthropic’s cloud infrastructure, not from your local machine. In the Claude apps under Customize > Connectors (or the in-chat “+” menu). Add a directory connector, or “Add custom connector” by URL.
    Plugin “A lightweight way to package and share any combination of” Claude Code customizations. Both — it’s a container. Bundles things that teach HOW (commands, skills) and things that give ACCESS (MCP servers), plus hooks and subagents. In Claude Code — “they’ll work across your terminal and VS Code.” The /plugin command (public beta). For a marketplace: /plugin marketplace add user-or-org/repo-name, then install from the /plugin menu.
    Hook “User-defined shell commands, HTTP endpoints, or LLM prompts that execute automatically at specific points in Claude Code’s lifecycle.” Controls behavior. Provides deterministic control rather than relying on the LLM to decide. In Claude Code, firing at lifecycle events (e.g. PreToolUse, PostToolUse, UserPromptSubmit, SessionStart, Stop). Configured in JSON settings files such as ~/.claude/settings.json or .claude/settings.json, or bundled in a plugin.
    Slash command A command starting with / that controls a Claude Code session. Includes built-ins (e.g. /help, /compact) and custom commands. Teaches HOW (custom) / controls session (built-in). Custom commands have been merged into Skills. In the Claude Code session (terminal or VS Code). Built-ins ship with Claude Code. Custom: a Markdown file under .claude/commands/ (project) or ~/.claude/commands/ (personal); a .claude/skills/<name>/SKILL.md does the same.

    Skills: teaching Claude a procedure

    Side-by-side cards defining what Claude Code is and is not
    Skills: teaching Claude a procedure.

    An Agent Skill is “a directory containing a SKILL.md file” with YAML frontmatter plus instructions, and optionally additional markdown files, executable scripts, and reference resources. The point is procedural knowledge — it turns “general-purpose agents into specialists” by giving Claude the workflows and best practices for a task, the way you’d write an onboarding guide for a new teammate. Anthropic ships pre-built Skills for PowerPoint, Excel, Word, and PDF, and you can author your own.

    What makes Skills cheap to install in bulk is progressive disclosure: Claude loads information in stages instead of all at once. The numbers below come straight from Anthropic’s Skills overview.

    Loading levelWhen loadedToken cost (per Anthropic docs)Content
    Level 1: MetadataAlways, at startup~100 tokens per Skillname and description from the YAML frontmatter
    Level 2: InstructionsWhen the Skill is triggeredUnder 5k tokensThe SKILL.md body — workflows and guidance
    Level 3+: ResourcesAs neededEffectively unlimitedBundled files read or executed via bash without loading their contents into context

    The name field is capped at 64 characters (lowercase letters, numbers, hyphens; it cannot contain the reserved words “anthropic” or “claude”), and the description is capped at 1,024 characters. One important constraint: custom Skills do not sync across surfaces — a Skill uploaded to claude.ai is not automatically available via the API, and Claude Code Skills are filesystem-based and separate from both.

    MCP: the open standard for access

    Flow from app/IDE through MCP to servers and data APIs
    MCP: the open standard for access.

    The Model Context Protocol is, in Anthropic’s words, “an open-source standard for connecting AI applications to external systems.” The canonical analogy: “Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems.” Using MCP, “AI applications like Claude or ChatGPT can connect to data sources (e.g. local files, databases), tools (e.g. search engines, calculators) and workflows (e.g. specialized prompts).”

    An MCP server can expose three kinds of building block — tools, resources, and prompts. In Claude Code, resources are referenced with @server:protocol://resource/path and prompts surface as commands in the form /mcp__servername__promptname. You connect a server with claude mcp add, choosing a transport and a scope:

    ScopeLoads inShared with teamStored in
    Local (default)Current project onlyNo~/.claude.json
    ProjectCurrent project onlyYes, via version control.mcp.json in project root
    UserAll your projectsNo~/.claude.json

    For transports, HTTP is “the recommended option for connecting to remote MCP servers,” local stdio servers “run as local processes on your machine,” and SSE is explicitly marked deprecated in favor of HTTP.

    Connectors: MCP, packaged for the apps

    A Connector is how the Claude apps surface MCP. Per Anthropic’s help center, “Connectors let Claude access your apps and services, retrieve your data, and take actions within connected services,” and “Custom connectors using remote MCP are available on Claude, Cowork, and Claude Desktop.” So a Connector is not a different technology from MCP — a custom connector is a remote MCP server wired into the Claude UI.

    The most consequential detail is where the connection originates: “Custom connectors (remote MCP servers) are reached from Anthropic’s cloud infrastructure, not from your local machine.” That means a custom-connector MCP server must be reachable over the public internet — one hosted only on a private network, behind a VPN, or blocked by a firewall will not connect even if you can reach it yourself.

    AspectDirectory (pre-built) connectorCustom connector
    SourcePre-built integrations in the Connectors DirectoryAdded by you via a remote MCP server URL
    Plan availabilityAvailable across Claude plansFree, Pro, Max, Team, and Enterprise
    Free-plan limitPer directory“Free users are limited to one custom connector.”
    Where to add itCustomize > Connectors, or the in-chat “+” > Connectors > Manage connectors

    Plugins: a bundle, not a single thing

    A Plugin is “a lightweight way to package and share any combination of” Claude Code customizations. The official announcement lists four bundle components, and the Claude Code documentation adds skills as a fifth thing a plugin can carry:

    ComponentWhat it adds
    Slash commandsCustom shortcuts for frequently-used operations
    SubagentsPurpose-built agents for specialized development tasks
    MCP serversConnections to tools and data sources through MCP
    HooksCustomizations of Claude Code’s behavior at key workflow points
    SkillsPer the Claude Code docs, a plugin can include a skills/ directory; plugin skills use a plugin-name:skill-name namespace

    You install a plugin “directly within Claude Code using the /plugin command.” To pull from a marketplace — “curated collections where other developers can discover and install plugins” — you run /plugin marketplace add user-or-org/repo-name and then install from the /plugin menu. Plugins “work across your terminal and VS Code.” Plugins were announced on October 9, 2025, as a public beta for all Claude Code users.

    Hooks and slash commands: controlling the session

    The last two mechanisms aren’t about adding capability — they’re about controlling a Claude Code session. Hooks are “user-defined shell commands, HTTP endpoints, or LLM prompts that execute automatically at specific points in Claude Code’s lifecycle.” Their defining property is determinism: they provide deterministic control rather than relying on the LLM to make decisions. A PreToolUse hook can, for example, block a destructive rm -rf command regardless of what Claude intended. Hooks are configured in JSON settings files (such as ~/.claude/settings.json or a project’s .claude/settings.json) and fire at events including SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, and Stop.

    Slash commands start with / and control the session. Built-in commands like /help and /compact ship with Claude Code. Custom commands are Markdown files — a project command lives at .claude/commands/<name>.md and a personal one at ~/.claude/commands/<name>.md, with the file name becoming the command. As of the 2026 Claude Code docs, custom commands have been merged into Skills: a file at .claude/commands/deploy.md and a skill at .claude/skills/deploy/SKILL.md “both create /deploy and work the same way,” and existing .claude/commands/ files keep working.

    Is a Connector the same as an MCP server?

    Effectively yes, for custom connectors. Anthropic states “Custom connectors using remote MCP are available on Claude, Cowork, and Claude Desktop,” and that they “are reached from Anthropic’s cloud infrastructure.” A Connector is the Claude-app packaging of a remote MCP server; MCP is the underlying open standard.

    What’s the difference between a Skill and an MCP server?

    A Skill teaches Claude how to do a task — it “provide[s] Claude with domain-specific expertise: workflows, context, and best practices.” An MCP server gives Claude access to external systems — it connects Claude “to data sources, tools and workflows.” One is procedural knowledge; the other is a connection.

    Do Skills cost a lot of context tokens?

    Not until used. Per Anthropic’s docs, Level 1 metadata costs about 100 tokens per Skill and is always loaded; the full SKILL.md body (under 5k tokens) only loads when the Skill is triggered; and bundled resources are read on demand with effectively no upfront cost. This is the “progressive disclosure” design.

    What can a Claude Code plugin contain?

    “Any combination of” slash commands, subagents, MCP servers, and hooks, per the announcement; the Claude Code documentation adds that a plugin can also bundle a skills/ directory. You install one with the /plugin command, optionally from a marketplace added via /plugin marketplace add.

    Are custom slash commands still a thing?

    They still work, but they’ve been folded into Skills. The Claude Code docs state custom commands “have been merged into skills,” that existing .claude/commands/ files keep working, and that a command file and an equivalent SKILL.md both produce the same / command. Skills add optional extras like supporting files and automatic invocation.

    Related on Tygart Media: what is MCP · Claude MCP guide.

  • Migrating Off Retired Claude Models: The Breaking-Chang (2026)

    Migrating Off Retired Claude Models: The Breaking-Chang (2026)

    Last verified: June 13, 2026

    Claude Opus 4 (claude-opus-4-20250514) and Claude Sonnet 4 (claude-sonnet-4-20250514) are deprecated and retire on June 15, 2026, after which requests to them return a 404. The official replacements are claude-opus-4-8 and claude-sonnet-4-6. But swapping the model string alone will break a working integration: depending on which target you choose, several request parameters that were valid on the May 2025 models now return a 400 error, and two changes alter behavior silently. This page maps each removed or changed parameter to the exact failure and the fix.

    One distinction governs the whole migration. The Opus path (to claude-opus-4-8) is the strict one: it removes temperature/top_p/top_k and manual thinking budgets entirely. The Sonnet path (to claude-sonnet-4-6) is gentler: it keeps sampling parameters (with the older “one of temperature or top_p, not both” rule) and still accepts budget_tokens as deprecated-but-functional. The one rule both paths share: assistant-turn prefills now return 400.

    The breaking-change matrix

    Side-by-side panels contrasting old model IDs still in code with a migrate checklist
    The breaking-change matrix.

    Each row is a change that breaks on at least one migration target. “Error” means the API rejects the request server-side (HTTP 400) even though the SDK request type still type-checks. “Silent” means no error — the behavior simply differs.

    ChangeOn Opus 4.8On Sonnet 4.6SymptomFix
    thinking: {type:"enabled", budget_tokens:N}400 error (removed)Deprecated, still works400 on Opus; cost/latency drift on Sonnetthinking: {type:"adaptive"} + output_config.effort
    temperature / top_p / top_k400 error (removed)Keep only one of temperature or top_p400 on Opus if any set; 400 on Sonnet if both setRemove on Opus; steer via prompt. Keep one on Sonnet
    Assistant-turn prefill (last message role:"assistant")400 error400 errorRequest rejected on bothoutput_config.format (structured outputs) or system-prompt instruction
    thinking.display defaultDefaults to "omitted"Returns summarized textReasoning text empty on Opus (silent)Set display: "summarized" on Opus
    TokenizerNew tokenizer (more tokens)Unchanged tokenizerSame text counts higher on Opus; max_tokens too tightRe-baseline with count_tokens; add headroom
    output_format (top-level)Deprecated API-wideDeprecated API-wideWorks, but slated for removalMove to output_config: {format: {...}}

    Model ID swaps and retirement dates

    Flow from Family to Seat to Generation to API id
    Model ID swaps and retirement dates.
    Retiring modelModel IDRetiresReplacement
    Claude Opus 4claude-opus-4-20250514 (alias claude-opus-4-0)June 15, 2026claude-opus-4-8
    Claude Sonnet 4claude-sonnet-4-20250514 (alias claude-sonnet-4-0)June 15, 2026claude-sonnet-4-6

    These are the original May 2025 models, not the later Opus 4.6 or Sonnet 4.5 releases. Use the exact replacement strings above — do not append a date suffix to claude-opus-4-8 or claude-sonnet-4-6 (they are dateless pinned snapshots).

    budget_tokens to adaptive thinking

    The Opus path removes the fixed thinking budget. thinking: {type:"enabled", budget_tokens:N} returns a 400 on claude-opus-4-8. The replacement is adaptive thinking — the model decides how much to think per request — with overall depth controlled by the effort parameter (low | medium | high | xhigh | max). There is no direct token-count equivalent; effort is an output-level control, not a thinking budget.

    # Before (Claude Opus 4 / Sonnet 4)
client.messages.create(
    model="claude-opus-4-20250514",
    max_tokens=16000,
    thinking={"type": "enabled", "budget_tokens": 10000},
    messages=[{"role": "user", "content": "..."}],
)

# After (Claude Opus 4.8)
client.messages.create(
    model="claude-opus-4-8",
    max_tokens=16000,
    thinking={"type": "adaptive"},
    output_config={"effort": "high"},  # or "max", "xhigh", "medium", "low"
    messages=[{"role": "user", "content": "..."}],
)

    On the Sonnet path, budget_tokens is deprecated but still functional on claude-sonnet-4-6, so it will not 400 — but you should still migrate to adaptive thinking. Note also that Sonnet 4.6 defaults to effort: "high" where Sonnet 4 had no effort parameter at all; if you do not set it explicitly you may see higher latency and token use after the swap.

    Sampling parameters: removed vs. restricted

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    Sampling parameters — removed vs restricted.

    This is where the two paths diverge most. On claude-opus-4-8, setting temperature, top_p, or top_k to any non-default value returns a 400. Remove them entirely and steer behavior through prompting instead. (If you used temperature=0 for determinism, note it never guaranteed identical outputs on prior models either.)

    # Opus path — sampling params 400 on claude-opus-4-8
# Before
client.messages.create(
    model="claude-opus-4-20250514",
    temperature=0.7,
    top_p=0.9,
    messages=[...],
)

# After — remove them
client.messages.create(
    model="claude-opus-4-8",
    messages=[...],
)

    On claude-sonnet-4-6 the older Claude 4.x rule still applies: you may pass one of temperature or top_p, but passing both returns a 400. So a Sonnet 4 to Sonnet 4.6 move only requires dropping one of the two if you were setting both.

    Assistant-turn prefills to structured outputs

    Prefilling the final assistant turn — ending your messages array with a role: "assistant" message to force a response shape — returns a 400 on both claude-opus-4-8 and claude-sonnet-4-6. This is the one breaking change you cannot dodge by choosing the gentler target. The replacement depends on what the prefill was doing.

    Prefill was used forReplacement
    Forcing JSON / YAML / schema outputoutput_config.format with a json_schema
    Forcing a classification labelA tool with an enum field, or structured outputs
    Skipping preambles (“Here is…”)System-prompt instruction: respond directly, no preamble
    Continuing an interrupted responseMove continuation into the user turn
    Steering around bad refusalsUsually unnecessary now — plain user-turn prompting suffices
     
    # Before (fails on both targets) — prefill forcing JSON shape
messages=[
    {"role": "user", "content": "Extract the name."},
    {"role": "assistant", "content": "{\"name\": \""},
]

# After — structured outputs replace the prefill
client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    output_config={"format": {"type": "json_schema", "schema": SCHEMA}},
    messages=[{"role": "user", "content": "Extract the name."}],
)

    Thinking display: the silent one

    On claude-opus-4-8, thinking blocks still stream, but their thinking text field is empty unless you opt in — the default is display: "omitted". There is no error; if your UI rendered the summarized reasoning, it now shows a long pause before output. Restore it by setting the display mode:

    thinking = {
    "type": "adaptive",
    "display": "summarized",  # default is "omitted" on Opus 4.8/4.7
}

    The block-field name is unchanged — it is still block.thinking on a thinking-type block. The fix is the request parameter, not the response-handling code. (Sonnet 4.6 is not affected by this default change.)

    The new tokenizer: re-baseline max_tokens

    This change is Opus-only and easy to miss because it produces no error. claude-opus-4-8 uses the tokenizer introduced with Opus 4.7, under which the same text tokenizes to roughly 1x–1.35x as many tokens — up to about 35% more, around 30% on typical content, varying by workload. Three consequences:

    What to checkWhy
    max_tokens ceilings and compaction triggersThe same output now consumes more tokens; tight limits truncate mid-thought
    Client-side token estimators (e.g. fixed char-to-token ratios)Calibrated against the old tokenizer; now undercount
    Cost and rate-limit dashboardscount_tokens returns higher numbers; re-baseline before reacting

    Re-run client.messages.count_tokens(model="claude-opus-4-8", ...) on a representative sample of your prompts. Do not apply a blanket multiplier. Sonnet 4.6 keeps the older tokenizer, so a Sonnet 4 to Sonnet 4.6 move has no tokenizer re-baseline to do.

    The full checklist

    StepOpus 4 to 4.8Sonnet 4 to 4.6
    Update model ID stringRequiredRequired
    Replace budget_tokens with adaptive thinkingRequired (400)Recommended (deprecated)
    Sampling paramsRemove all (400)Keep only one (both 400)
    Remove assistant-turn prefillsRequired (400)Required (400)
    Set display: "summarized" if showing reasoningRequired for visible thinkingNot applicable
    Re-baseline max_tokens for new tokenizerRequiredNot applicable
    Set effort explicitlyDefaults to highDefaults to high
    Move output_format to output_config.formatRecommendedRecommended
    Verify tool inputs parsed with a JSON parserRecommendedRecommended
    Spot-check one request, then roll outRequiredRequired

    If you run Claude Code, /claude-api migrate applies the model swap, breaking-parameter changes, prefill replacement, and effort calibration across a codebase, then produces a verify-it-yourself checklist. It asks you to confirm scope before editing any files.

    Is migrating off Claude Opus 4 really not just a model-string change?

    No. Moving to claude-opus-4-8 also requires removing temperature/top_p/top_k and any budget_tokens (all now return 400), removing assistant-turn prefills (400), opting back into summarized thinking if your UI shows it, and re-baselining max_tokens for the new tokenizer. Only the Sonnet 4 to Sonnet 4.6 move is close to a drop-in — and even that requires removing prefills.

    When exactly do Claude Opus 4 and Sonnet 4 stop working?

    June 15, 2026. After that date, requests to claude-opus-4-20250514 and claude-sonnet-4-20250514 return a 404. These are the original May 2025 models, not Opus 4.6 or Sonnet 4.5.

    What replaces budget_tokens now that it errors on Opus?

    Adaptive thinking (thinking: {type:"adaptive"}) plus the effort parameter inside output_config. There is no exact token-count equivalent: the model decides how much to think per request, and effort (low through max) tunes overall depth and spend. On Sonnet 4.6, budget_tokens still works but is deprecated.

    Why does the same prompt cost more tokens on Opus 4.8?

    Opus 4.8 uses the tokenizer introduced with Opus 4.7, under which the same text produces roughly 1x–1.35x as many tokens (about 30% more on typical content, up to ~35%). Re-run the count_tokens endpoint against claude-opus-4-8 and give max_tokens and compaction triggers extra headroom. Sonnet 4.6 keeps the older tokenizer, so it is unaffected.

    My thinking summaries disappeared after migrating to Opus — is that a bug?

    No. On Opus 4.8 (and 4.7), thinking.display defaults to "omitted", so thinking blocks stream with an empty text field. Set display: "summarized" in your thinking config to restore visible reasoning. The field name is unchanged; only the default flipped.

    Related on Tygart Media: Claude Fable 5 guide · API quickstart.

  • Latest Claude Models — moved to the tracker

    Latest Claude Models — moved to the tracker

    This slug duplicates the ranking tracker. Numbers here were last written as June 2026.

    Direct Answer (9 September 2026): Claude 3.5 Sonnet is retired. Current public Sonnet is Sonnet 5 at $2 / $10 per MTok. Current lineup is Fable 5.1, Opus 5, Sonnet 5, Haiku 4.5. Live tracker: current Claude model version. Seat dollars: pricing hub.

    Do not ship 3.5 Sonnet, Sonnet 4.6, or Opus 4.8 IDs in new work. Confirm the alias in the console.

  • Claude Code Getting Started: Installation, Firs (2026)

    Claude Code Getting Started: Installation, Firs (2026)

    Claude Code is Anthropic’s official CLI for Claude — a terminal-based agent you can point at any codebase and have it read, write, test, and ship code. It’s different from the Claude.ai chat interface in one key way: Claude Code can act, not just answer. It reads your actual files, runs your actual commands, and makes changes that stick.

    This guide walks you through installation, first run, and the commands that cover 90% of what you’ll do daily.

    What Claude Code Is (and Isn’t)

    Side-by-side cards defining what Claude Code is and is not
    Claude Code is a terminal agent — not a chat website.

    Claude Code runs in your terminal. It gives Claude access to your local machine — file system, shell, and any MCP servers you configure — so it can do real engineering work: implement features, fix bugs, write tests, explain unfamiliar codebases, and run multi-step agentic workflows.

    It is not a code autocomplete plugin (that’s what GitHub Copilot does). Claude Code is a conversational agent that works at the task level, not the token level. You describe what you want; it figures out the steps and executes them.

    Installation

    Claude Code requires Node.js 18 or later. Install via npm:

    npm install -g @anthropic-ai/claude-code

    Verify the install:

    claude --version

    That’s the only dependency. Claude Code is a Node.js CLI — no Docker, no Python env, no platform-specific setup beyond Node.

    First-Run Authentication

    The first time you run claude, it walks you through authentication. You have two options:

    Option 1: Claude subscription (Pro, Max, Team, Enterprise)
    Run claude, select “Login with Claude.ai,” and it opens a browser window to authorize. Your subscription covers Claude Code usage — no separate API billing.

    Option 2: Anthropic API key
    Set your API key as an environment variable before running:

    export ANTHROPIC_API_KEY="sk-ant-..."
claude

    Or on Windows:

    $env:ANTHROPIC_API_KEY = "sk-ant-..."
claude

    API key usage is billed per token at standard API rates. For heavy daily use, a Max subscription ($100–$200/month) is usually more economical than API billing.

    Your First Session

    Navigate to a project directory and start Claude Code:

    cd ~/projects/my-app
claude

    Claude Code reads your directory automatically. At the > prompt, describe what you want:

    > What does this codebase do? Give me a 3-paragraph overview.

    Claude reads the files it needs and responds. No configuration required for basic usage — Claude Code infers context from the directory you’re in.

    The 5 Commands You’ll Use Daily

    Five stacked panels of daily Claude Code command habits
    The five commands you’ll use daily.

    1. claude — Start an interactive session

    claude

    Launches the REPL (read-eval-print loop). This is where you spend most of your time. Claude has access to your current directory’s files, can run bash commands, and can call any MCP servers you’ve configured.

    Within a session, you can:

    • Ask questions about the codebase
    • Request implementations (“add a rate limiter to the auth middleware”)
    • Have Claude run tests and fix failures
    • Use /help to see available slash commands
    • Use /clear to reset context without leaving the session
    • Press Escape twice to interrupt a running task

    2. claude -p "prompt" — One-shot non-interactive mode

    claude -p "What are all the API endpoints in this codebase?"

    Runs a single prompt and exits. No REPL. Good for scripting, CI pipelines, or quick one-off queries you don’t want to interrupt a workflow for. Output goes to stdout — pipe it wherever you need it.

    claude -p "Summarize the changes in the last 10 commits" | pbcopy

    3. claude mcp add — Connect an external tool

    claude mcp add github -- npx -y @modelcontextprotocol/server-github

    Adds an MCP server to your Claude Code configuration. After running this, Claude can call the server’s tools in any session. Common additions:

    # File system access (scoped to a directory)
claude mcp add files -- npx -y @modelcontextprotocol/server-filesystem ~/Documents

# GitHub integration
claude mcp add github -- npx -y @modelcontextprotocol/server-github

# Web search
claude mcp add search -- npx -y @modelcontextprotocol/server-brave-search

    The GitHub and Brave Search servers need API tokens — set them as environment variables before the server starts, or pass them via the --env flag in the mcp add command.

    4. claude -c — Continue the last conversation

    claude -c

    Resumes your most recent Claude Code conversation, including all prior context. Essential for multi-session work on a feature. If you closed the terminal mid-task, claude -c picks up exactly where you left off.

    For a specific prior conversation:

    claude --resume SESSION_ID

    5. claude --model — Select the model for a session

    claude --model claude-opus-4-8

    Claude Code defaults to the most capable available model for your plan. You can override this per session. Current options:

    • claude-fable-5 — Highest capability, complex tasks (2x cost vs Opus 4.8)
    • claude-opus-4-8 — Default for most work, strong balance of quality and speed
    • claude-sonnet-4-6 — Faster responses, good for routine tasks
    • claude-haiku-4-5-20251001 — Fastest, lowest cost, short tasks

    Slash Commands Inside a Session

    While in a Claude Code session (> prompt), these slash commands are available:

    CommandWhat It Does
    /helpShow all available commands
    /clearClear conversation context (keep the session open)
    /compactCompress prior context to save tokens while preserving essential memory
    /costShow token usage and estimated cost for the current session
    /modelSwitch the model mid-session
    /reviewRequest a multi-agent code review of the current branch
    /initGenerate a CLAUDE.md file with project context for this repo
    /exitEnd the session

    CLAUDE.md — Project-Level Context

    Drop a CLAUDE.md file in your project root and Claude Code reads it automatically at session start. Use it to encode project-specific context Claude shouldn’t have to re-derive every session:

    # My Project

## Architecture
- Backend: FastAPI + PostgreSQL
- Frontend: React + TypeScript
- Deployed to: AWS ECS

## Development
- Tests: `pytest tests/`
- Local server: `./scripts/start-dev.sh`
- Database migrations: `alembic upgrade head`

## Rules
- Never modify migration files directly
- All API routes go in `src/routes/`
- Use `httpx` not `requests` for HTTP calls

    Generate a starter CLAUDE.md for an existing project with /init.

    Permission Modes

    Five-step path: account, API keys, billing, usage, workspaces
    Permission modes — decide what the agent may touch.

    Claude Code asks for confirmation before running bash commands, creating files, or making other changes — unless you grant it broader permissions. There are three ways to control this:

    • Default: Claude asks before each tool use that modifies files or runs commands
    • --dangerously-skip-permissions: Skip all confirmations. Use only in isolated environments (Docker containers, CI). Not for everyday use on your primary machine.
    • Session-level allowlist: During a session, you can approve individual tools for the rest of the session by selecting “Allow always” when prompted

    For most work, the default confirmation behavior is the right trade-off — it keeps you in the loop on changes without requiring you to pre-define a permission policy.

    IDE Integration

    Claude Code integrates with VS Code and JetBrains IDEs. Install the extension from each marketplace, then launch Claude Code from inside the IDE. This keeps the terminal panel visible alongside your editor without alt-tabbing between windows.

    The IDE extensions also add shortcuts for common actions like opening Claude Code in the current file’s directory and running one-shot queries against the selected code.

    Related on Tygart Media: Claude Code billing · Claude Code vs Cursor · Claude Code tutorial.

    Frequently Asked Questions

    What’s the difference between Claude Code and Claude.ai?
    Claude.ai is the web chat interface — good for questions, document analysis, and writing. Claude Code is a terminal CLI that can access your local files, run commands, and act autonomously on multi-step tasks. Claude.ai can’t modify files on your machine; Claude Code can.

    Does Claude Code cost extra on top of my Claude subscription?
    No. Claude Pro, Max, Team, and Enterprise subscriptions include Claude Code access. You use the same account. Heavy agentic usage counts toward the plan’s usage limits, but there’s no separate Claude Code fee.

    Can Claude Code access the internet?
    Not by default. Claude Code’s built-in WebFetch tool can fetch content from a specific URL when you provide it. For live web search, add the Brave Search or similar MCP server. Claude can’t browse freely without explicit tool access.

    What does Claude Code do with my code?
    Claude Code sends the file contents and context it needs to the Anthropic API for inference. Standard Anthropic API data policies apply — if you’re using an API key, you can configure zero data retention. If you’re using a subscription, default Anthropic retention policies apply. Review Anthropic’s privacy policy for current details.

    Is Claude Code open source?
    Claude Code itself (the CLI client) is not open source — it’s an Anthropic product. The MCP server ecosystem it connects to includes many open-source servers, and the MCP specification itself is open.

    What version of Node.js do I need?
    Node.js 18 or later. Run node --version to check. The Long-Term Support (LTS) version is always a safe choice.

    Last verified: June 12, 2026. Claude Code is updated frequently — run npm update -g @anthropic-ai/claude-code to stay current.

  • What Is Model Context Protocol (MCP)? The Complete Guid (2026)

    What Is Model Context Protocol (MCP)? The Complete Guid (2026)

    Model Context Protocol (MCP) is the reason Claude can read your files, query your database, search the web, and push code to GitHub — all from inside a single conversation. Without it, Claude would be limited to whatever you paste in manually. With it, Claude connects to almost any external system.

    Quick answer: MCP is an open standard developed by Anthropic that lets AI models securely connect to external tools, data sources, and services through a standard client-server architecture. You install an MCP server for the system you want Claude to access. Claude becomes a client that calls that server. The server executes the action and returns results.

    The Problem MCP Solves

    Flow from app/IDE through MCP to servers and data APIs
    The problem MCP solves: one protocol, many tools.

    Before MCP, connecting an AI model to external data meant one of two things: either the AI company built a native integration (slow, expensive, proprietary), or you cobbled together a pipeline that passed data manually between systems.

    Neither approach scales. If Claude natively supported every database, every API, every file format, and every SaaS tool on the planet, the model would be perpetually behind. And manual copy-paste workflows aren’t agentic — they require you to do all the coordination work the AI should be doing.

    MCP solves this with a universal adapter layer. Instead of building individual integrations, Anthropic defined a standard. Now any developer can build an MCP server for any system, and any MCP-compatible AI client (like Claude) can use it automatically.

    How MCP Works

    Three stacked layers: chat UI, tools, agent runtime
    How MCP works as a layer between tools and agents.

    MCP uses a client-server model over two transport mechanisms:

    • stdio: The MCP server runs as a local subprocess on your machine. Claude Code spawns it, communicates via standard input/output. This is the most common setup.
    • HTTP/SSE: The MCP server runs as a network service. Claude connects over HTTP with Server-Sent Events for streaming. Better for remote or shared servers.

    The communication protocol underneath is JSON-RPC 2.0 — a lightweight, well-understood standard for calling methods and getting results.

    Each MCP server exposes one or more of three primitives:

    • Tools: Functions Claude can call. Example: read_file(path), create_issue(title, body), run_query(sql). Claude decides when to call them based on context.
    • Resources: Data sources Claude can read. Example: the contents of a directory, a database schema, a project’s README. Resources are passive — they don’t take actions, they expose information.
    • Prompts: Reusable prompt templates that servers can provide to standardize how Claude interacts with them.

    When Claude sees a task that could benefit from an available tool, it calls the tool, receives the result, and incorporates it into the response. This happens automatically — you don’t have to tell Claude when to use MCP. Claude decides based on what the server exposes.

    MCP in Claude Code vs Claude Desktop

    Both Claude Code (the CLI tool) and Claude Desktop support MCP, but they configure servers differently.

    Claude Code

    Claude Code has built-in MCP management via the claude mcp command family:

    claude mcp add my-server -- npx -y @modelcontextprotocol/server-filesystem /path/to/directory
claude mcp list
claude mcp remove my-server

    Servers added with claude mcp add are stored in your Claude Code config (~/.claude.json or the project-level .claude/settings.json). Project-level configs let you commit MCP server setups to source control so the whole team gets them automatically.

    Claude Code also ships with a set of built-in tools that behave like MCP servers but don’t require separate installation: file read/write/edit, bash execution, glob search, grep, web fetch, and the agent spawning tools you’re reading about in this article.

    Claude Desktop

    Claude Desktop reads MCP server configuration from a JSON file:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json

    A typical config entry looks like this:

    {
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/you/Documents"]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_your_token_here"
      }
    }
  }
}

    Restart Claude Desktop after editing the config. Each server you add appears in the Claude Desktop interface with a hammer icon, and Claude can access its tools in any conversation.

    The Most Useful MCP Servers

    Anthropic maintains a reference set of official MCP servers. These are the ones worth knowing:

    ServerWhat It DoesPackage
    FilesystemRead/write files and directories on your local machine@modelcontextprotocol/server-filesystem
    GitHubRead repos, create issues, open PRs, push code@modelcontextprotocol/server-github
    PostgreSQLRead-only SQL queries against a Postgres database@modelcontextprotocol/server-postgres
    SQLiteRead/write a local SQLite database file@modelcontextprotocol/server-sqlite
    Brave SearchLive web search via Brave’s Search API@modelcontextprotocol/server-brave-search
    PuppeteerHeadless browser — screenshot pages, scrape, fill forms@modelcontextprotocol/server-puppeteer
    SlackRead channels, send messages, search workspace@modelcontextprotocol/server-slack
    Google DriveRead and search Google Drive files@modelcontextprotocol/server-google-drive
    GitGit operations — log, diff, commit, branch management@modelcontextprotocol/server-git
    MemoryPersistent key-value knowledge graph across conversations@modelcontextprotocol/server-memory

    Beyond the official set, hundreds of community-built MCP servers cover everything from Notion and Linear to AWS and Docker. The MCP ecosystem grew faster than almost anyone expected after the November 2024 launch.

    Installing Your First MCP Server

    The fastest path is Claude Code with the filesystem server. This gives Claude read/write access to a directory you specify — useful for any project work.

    Prerequisites: Node.js installed (the server runs via npx).

    In your terminal:

    claude mcp add filesystem -- npx -y @modelcontextprotocol/server-filesystem ~/Documents/projects

    That’s it. Open a Claude Code session. Claude can now list, read, write, and search files inside ~/Documents/projects. Try: “List all Python files in this directory and summarize what each one does.”

    For Claude Desktop, edit the claude_desktop_config.json file directly (see format above), then restart the app.

    What MCP Cannot Do

    Five red warning rows of MCP limitations
    What MCP cannot do — permissions and review still matter.

    A few things worth understanding before you build on MCP:

    MCP servers don’t persist between conversations. Each Claude session starts fresh. If you need state persistence, you need a server with its own storage layer (the Memory server handles this specifically).

    MCP doesn’t bypass Claude’s safety guidelines. Claude still decides whether to execute a tool call based on safety and ethics reasoning. Connecting a filesystem server doesn’t give Claude unlimited license to delete files — Claude will still confirm before destructive operations.

    Subprocess MCP servers are local. The stdio transport runs servers on your machine. This means they only work when you’re running Claude Code locally. For remote or team-shared access, you need HTTP/SSE transport with a hosted server.

    Security Considerations

    MCP servers have real permissions. The filesystem server can read and write files. The GitHub server can push code to your repos. The Postgres server can run SQL queries.

    Apply the principle of least privilege:

    • Scope filesystem servers to the directory you actually need, not /
    • Use read-only database credentials where you don’t need writes
    • Create GitHub tokens with minimum required scope (e.g., repo for private repos, not org-level admin)
    • Never commit environment variables containing API keys to source control, even in .claude/settings.json — use env var references instead

    MCP servers run with the permissions of the user running Claude. If something goes wrong with a tool call, it can have real consequences. The upside: everything runs locally and through your own credentials — there’s no MCP cloud intermediary with access to your data.

    MCP and Claude Code’s Agentic Workflows

    The full power of MCP shows up in Claude Code’s multi-step agentic mode. When Claude Code has access to git, a filesystem, a browser, and a search tool simultaneously, it can execute workflows like:

    1. Search the web for a library’s current API (Brave Search)
    2. Read your existing code to understand the integration point (filesystem)
    3. Write the updated code (filesystem write)
    4. Run tests (bash)
    5. Create a PR (GitHub)

    Each of these steps would require a separate tool in a traditional automation stack. With MCP, Claude orchestrates all of them within a single session, using whatever servers are available.

    This is what makes MCP the infrastructure layer for agentic AI — not a feature, but the foundation that makes complex AI-driven workflows possible.

    Related on Tygart Media: Claude MCP · Skills vs MCP vs plugins · how to use Claude.

    Frequently Asked Questions

    What does MCP stand for?
    Model Context Protocol. It’s an open standard for connecting AI models to external tools, data sources, and services through a standard client-server interface.

    Who created MCP?
    Anthropic created MCP and released it as an open standard in November 2024. The specification and reference servers are open-source on GitHub. While Claude is the primary client, other AI systems can implement MCP clients too.

    Do I need to install MCP to use Claude?
    No. Claude works without any MCP servers. MCP is an extension layer — you add servers when you want Claude to access specific external systems. Claude Code also ships with a set of built-in tools (file operations, bash, web fetch) that don’t require MCP installation.

    Is MCP available on Claude.ai (the web app)?
    MCP server support is primarily in Claude Desktop and Claude Code. The Claude.ai web interface has its own tool integrations (web search, document analysis) but doesn’t support custom MCP servers in the same way.

    What’s the difference between MCP tools and Claude’s native tools in Claude Code?
    Claude Code’s native tools (Read, Write, Bash, Glob, Grep, WebFetch, Agent) are built into the application and don’t require a separate server process. MCP servers are external — they run as subprocesses or network services that Claude Code connects to. Both expose tools that Claude can call; the mechanism for loading them is different.

    How do I build my own MCP server?
    Anthropic provides official SDKs for building MCP servers in TypeScript, Python, Go, and other languages. The TypeScript SDK (@modelcontextprotocol/sdk) is the most mature. Start with Anthropic’s MCP documentation and the reference server implementations on GitHub as templates.

    Last verified: June 12, 2026. MCP specification and server ecosystem evolve quickly — check the official Anthropic MCP documentation for the current spec.

  • Claude Fable 5: Capabilities, Pric (2026)

    Claude Fable 5: Capabilities, Pric (2026)

    Anthropic released Claude Fable 5 on June 9, 2026 — and it’s the most capable model the company has ever made publicly available. After tracking every Claude release since the original 100K context window dropped, I can say this one is different. Fable 5 isn’t just an incremental update. It’s Anthropic’s Mythos-class model — the one they’d been keeping restricted — now opened up to anyone with an API key or a Claude subscription.

    Here’s what you need to know: the pricing, the benchmarks, and the specific decision framework for when to use Fable 5 versus sticking with Opus 4.8.

    Quick answer: Fable 5 costs $10/$50 per million input/output tokens (2x the cost of Opus 4.8). It outperforms Opus 4.8 significantly on complex coding, long-horizon tasks, and scientific research. Use Fable 5 when quality on hard problems justifies the cost. Use Opus 4.8 for high-volume, well-scoped, routine work.

    What Is Claude Fable 5?

    Three cards for fast volume, daily workhorse, and deep flagship Claude seats
    What is Claude Fable 5?

    Claude Fable 5 (claude-fable-5) is Anthropic’s first publicly available Mythos-class model. The Mythos line is Anthropic’s highest capability tier — models that were previously restricted to research and select enterprise partners because of their raw power. Fable 5 is the version Anthropic deemed safe enough to release broadly.

    The name shift (from the Opus/Sonnet/Haiku tier naming) signals something intentional. Fable 5 sits above the Opus line entirely. It’s a new ceiling.

    Key specs:

    • Context window: 1M tokens (same as Opus 4.8)
    • Max output: 128K tokens per request
    • Thinking: Adaptive (always on — not a separate “thinking mode”)
    • Vision: Yes
    • Tool use / function calling: Yes
    • Available: Claude API, AWS Bedrock, Vertex AI, Microsoft Foundry

    Claude Fable 5 Pricing

    Infographic ladder of Claude plans: Free, Pro, Max, Team, and Enterprise
    Fable 5 pricing — no sticky dollar stickers.
    ModelInput (per MTok)Output (per MTok)Context
    Claude Fable 5$10.00$50.001M tokens
    Claude Opus 4.8$5.00$25.001M tokens
    Claude Sonnet 4.6$3.00$15.001M tokens
    Claude Haiku 4.5$1.00$5.00200K tokens

    Fable 5 costs exactly 2x Opus 4.8 on API. On subscription plans (Pro, Max, Team, Enterprise seat-based), Fable 5 is included at no extra cost through June 22, 2026.

    The free-until-June-22 window matters if you’re evaluating whether to route your workloads to Fable 5. Use that window to benchmark it against your actual tasks before the 2x cost kicks in.

    Benchmark Performance: Where Fable 5 Pulls Away

    The benchmarks that matter most are the ones that measure what the model can do on real engineering work, not trivia:

    BenchmarkClaude Fable 5Claude Opus 4.8Delta
    SWE-bench Verified95.0%88.6%+6.4 pts
    SWE-bench Pro80.0%69.2%+10.8 pts
    FrontierCode29.3%13.4%~2.2x
    Senior Engineer benchmark91/100~63/100+45% absolute

    The Senior Engineer benchmark is the one I find most telling. It’s designed to be hard for people who write code for a living — and Fable 5 scores 45 percentage points higher than Opus 4.8. That gap is significant enough that it changes the calculus for serious engineering work.

    When to Use Claude Fable 5 (vs Opus 4.8)

    Decision fork between maximum capability when stakes are high and shipping daily when speed and cost matter
    When to use Fable 5 vs Opus.

    I’ve been routing tasks between models for long enough to have a framework. Here’s how I think about it:

    Use Fable 5 when:

    • You’re running a large migration, refactor, or multi-stage software project
    • Quality on a hard problem matters more than per-token cost
    • You’re doing deep research, complex analysis, or long-horizon agentic work
    • The task would otherwise take a senior engineer half a day or more
    • You’re in the free evaluation window (through June 22) and want to benchmark

    Use Opus 4.8 when:

    • The task is well-scoped and routine
    • You’re running high-volume pipelines where 2x cost compounds fast
    • Latency matters — Fable 5 can take 60 seconds to several minutes on complex tasks vs 3–15 seconds for Opus 4.8
    • The task falls in Fable 5’s restricted domains (cybersecurity, biology, chemistry, distillation) — in those categories, Fable 5 routes to Opus 4.8 anyway, so you’d pay Fable 5 prices for Opus 4.8 output

    The smart routing strategy: Fable 5 for the hard jobs, Opus 4.8 for the rest. Don’t use Fable 5 as your default model — the cost and latency delta aren’t worth it for routine tasks.

    Important Limitations to Know Before You Switch

    Two limitations that don’t get enough coverage:

    1. Safety classifier routing. Fable 5 includes enhanced safety classifiers. For prompts touching cybersecurity, biology, chemistry, and distillation, those classifiers route the request to a Claude Opus 4.8 fallback. You pay Fable 5 API rates ($10/$50) but get Opus 4.8 output. If your use case is in these domains, Fable 5 is not the upgrade it appears to be.

    2. Data retention requirement. Fable 5 carries a mandatory 30-day data retention policy — Anthropic needs retained prompts and outputs to operate the safety classifiers. Claude Opus 4.8 is available under zero data retention (ZDR). If your use case requires ZDR (healthcare, legal, finance with strict data handling), stick with Opus 4.8 until Anthropic updates Fable 5’s data policy.

    Availability

    Claude Fable 5 is generally available as of June 9, 2026 on:

    • Claude API (claude-fable-5)
    • Claude Platform on AWS / Amazon Bedrock
    • Google Cloud Vertex AI
    • Microsoft Azure AI Foundry / GitHub Copilot

    Subscription access (free through June 22, 2026): Claude Pro ($20/mo), Max 5x ($100/mo), Max 20x ($200/mo), Team, and seat-based Enterprise plans all include Fable 5 access at no extra charge during the launch window. After June 22, the plan-tier access picture may change — check Anthropic’s pricing page for updates.

    How This Changes the Claude Model Decision Tree

    Before Fable 5, the Claude decision tree was straightforward:

    • Need the best? → Opus 4.8
    • Need balance? → Sonnet 4.6
    • Need speed/cost? → Haiku 4.5

    Now it’s:

    • Hard problems, complex projects, long-horizon work → Fable 5
    • Everyday work, high-volume pipelines → Opus 4.8
    • Balance of cost and capability → Sonnet 4.6
    • Speed and cost optimization → Haiku 4.5

    The introduction of a model tier above Opus 4.8 doesn’t replace the existing lineup — it creates a new ceiling for the work that genuinely needs it.

    Related on Tygart Media: Fable 5 complete guide · Fable 5 firsthand · how to use Claude.

    Frequently Asked Questions

    Is Claude Fable 5 better than Opus 4.8?
    For complex coding, multi-stage tasks, and long-horizon work: yes, significantly. On SWE-bench Pro, Fable 5 scores 80.0% vs Opus 4.8’s 69.2% — a 10+ point gap. For routine, well-scoped tasks: the gap narrows enough that Opus 4.8’s 2x cost advantage makes it the smarter choice.

    What is the Claude Fable 5 API model ID?
    claude-fable-5. This is the API string you pass to model in your API calls.

    Does Fable 5 cost more than Opus 4.8?
    Yes — exactly 2x. Fable 5 is $10 input / $50 output per million tokens. Opus 4.8 is $5/$25. Through June 22, 2026, Fable 5 is included in Claude subscription plans at no extra cost.

    Can I use Claude Fable 5 for free?
    On Pro, Max, Team, and Enterprise subscription plans, yes — through June 22, 2026. API access is metered at $10/$50 per MTok from day one.

    Does Claude Fable 5 support zero data retention (ZDR)?
    No. Fable 5 carries a mandatory 30-day data retention requirement. If your use case requires ZDR, use Claude Opus 4.8, which supports it.

    What’s the difference between Claude Fable 5 and Claude Mythos 5?
    Mythos 5 is Anthropic’s fully restricted research model — not publicly available. Fable 5 is the Mythos-class model that Anthropic has prepared for general availability, with safety classifiers and the 30-day retention policy. You can think of Fable 5 as “Mythos for the real world.”

    Last verified: June 12, 2026. Anthropic pricing and availability subject to change — check Anthropic’s pricing page for current rates.

  • How to Get an Anthropic API Key (2026 Quick Guide)

    How to Get an Anthropic API Key (2026 Quick Guide)

    Last verified: August 26, 2026 (Pacific Time)

    Direct Answer (August 2026): To obtain an Anthropic API key: 1) Register at console.anthropic.com, 2) Navigate to Settings > Plans & Billing and deposit $5+ for Tier 1 activation, 3) Go to ‘API Keys’, click ‘Create Key’, and copy your secure sk-ant-api03-... key token.

    macOS / Linux:
export ANTHROPIC_API_KEY="sk-ant-YOUR_KEY_HERE"

Windows (PowerShell):
setx ANTHROPIC_API_KEY "sk-ant-YOUR_KEY_HERE"

    Replace YOUR_KEY_HERE with the key shown once at creation — Anthropic never shows it again.

    An Anthropic API key is the secret token that authenticates your requests to Claude models over the API. It starts with sk-ant-, is created in the Anthropic Console at console.anthropic.com under API Keys → Create Key, and is displayed exactly once — requests only succeed after you add a payment method.

    Official links: Create a key (console) · API docs · API rates · Help center

    Quick answer: sign in at console.anthropic.com (it now redirects to the same developer console as platform.claude.com), add a payment method under Settings → Billing, click API Keys → Create Key, name it, and copy it immediately – Anthropic shows the key exactly once. Keys start with sk-ant-. The whole process takes about five minutes.

    Below is the full walkthrough, where to put the key so it doesn’t leak, the newer no-static-key option most tutorials haven’t caught up with, and the errors that account for nearly every failed first request. For the full reference – pricing tiers, key rotation, security, and workspace and organization keys – see our Anthropic API key reference and management guide.

    What you need before you start

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    Account, billing, and a place to store the secret — before you click Create.
    • An email address (or Google / SSO login)
    • A payment method – your key will not work until billing is set up, even though you can create one
    • Five minutes

    One distinction that confuses almost everyone: a Claude.ai subscription is not API access. Claude Pro, Max, and Team plans cover the Claude apps (web, desktop, mobile). The API is billed separately, by usage, through the developer console. You can have either one without the other – see our complete Claude pricing guide for how the two systems differ.

    Step 1: Create your account

    Open the Anthropic console ?

    Go to console.anthropic.com – Anthropic’s developer console. (Both console.anthropic.com and platform.claude.com land in the same place in 2026; older tutorials treat them as different sites.) Sign up with email, Google, or SSO, and answer the brief onboarding questions about whether you’re an individual or an organization. For a tour of everything inside the console, see our Anthropic Console guide.

    Step 2: Add billing

    Open billing settings ?

    In the console, open Settings → Billing and add a credit card (self-serve accounts typically purchase prepaid usage credits). Skipping this step is the #1 reason a brand-new key returns errors – the key exists, but requests are rejected until the account can be billed.

    Step 3: Create the key

    Create the key in the console ?

    Click API Keys in the left sidebar (direct link: platform.claude.com/settings/keys), then Create Key. Give it a descriptive name like my-app-dev – future you will thank present you when it’s time to rotate or revoke. If your organization uses multiple workspaces, note that keys are scoped to a workspace: the key only sees resources in the workspace it was created in.

    Step 4: Copy it immediately

    Developer copying a new API key into a password vault; secret string not readable
    Copy once. Close the dialog. If you lose it, rotate — do not paste it into chat.

    The key is displayed exactly once. It starts with sk-ant- followed by a long string. Copy it straight into a password manager, a .env file, or your secrets manager. If you lose it, there is no way to view it again – you revoke it and create a new one (takes a minute, harms nothing).

    Where to put the key (and where never to put it)

    Diagram contrasting never places for API keys versus vault, env file, and secrets manager
    Vault / env / secrets manager. Never chat, git, or a screenshot thread.

    Set it as an environment variable named ANTHROPIC_API_KEY – every official Anthropic SDK reads that variable automatically, so your code never contains the key:

    • macOS / Linux: export ANTHROPIC_API_KEY=sk-ant-...
    • Windows (PowerShell): setx ANTHROPIC_API_KEY "sk-ant-..."
    • Python: client = anthropic.Anthropic() – no key argument needed
    • TypeScript: const client = new Anthropic() – same

    Never hardcode the key in source files, never commit it to a repository, and never paste it into a system prompt or chat message. Leaked Anthropic keys get scraped and drained like any other credential.

    The 2026 no-key option: OAuth login

    Newer than most guides: Anthropic’s CLI can authenticate without any static key. Run ant auth login and a browser window authorizes a short-lived OAuth profile on your machine – the SDKs and Claude Code pick it up automatically, and there is no permanent secret to leak or rotate. For CI servers and production workloads, Workload Identity Federation serves the same purpose. If you’re setting up a personal development machine in 2026, this is arguably the better default; create a static key when you need one for a deployed service.

    Test your key

    One request confirms everything works (Haiku keeps the test nearly free):

    curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{"model": "claude-haiku-4-5", "max_tokens": 32, "messages": [{"role": "user", "content": "Say hello"}]}'

    A JSON response with a content array means you’re live.

    Troubleshooting the four common errors

    • 401 authentication_error – the key is missing, mistyped, or revoked. Subtle 2026 variant: if both ANTHROPIC_API_KEY and ANTHROPIC_AUTH_TOKEN are set, the SDK sends both and the API rejects the request – unset one.
    • 403 permission_error – the key works but lacks access to that model or feature; check your key’s workspace and your organization’s model access.
    • 429 rate_limit_error – you’re sending faster than your usage tier allows. The response includes a retry-after header; official SDKs retry automatically. For tier details and fixes, see our Claude rate limits guide.
    • Key created but every request fails – almost always billing not completed (Step 2).

    FAQ

    Is the Anthropic API free? No – it’s usage-priced per million tokens with no permanent free tier (current rates in our Claude pricing guide, including the June 2026 lineup with Fable 5).

    Where do I find my existing API key? You can’t – Anthropic shows keys only at creation. Revoke the old one and create a replacement.

    Where is the API Keys page in console.anthropic.com? Sign in at console.anthropic.com, choose API Keys in the left navigation — or go straight to platform.claude.com/settings/keys — then click Create Key.

    Does my Claude Pro or Max subscription include an API key? No. App subscriptions and API billing are separate systems; an API account starts at $0 and bills per token used.

    What models can a new key use? The current lineup as of June 2026 – including Claude Fable 5, Opus 4.8, Sonnet 4.6, and Haiku 4.5; see everything that changed in June 2026.

    Get alerted when Claude pricing or limits change

    We track Anthropic’s models, pricing, and limits daily and send a short note when something changes that affects what you pay or build. Occasional, no spam.

    Subscription Form

    Sources

    💼 Deploying Claude or AI Infrastructure in Your Business?

    At Tygart Media, we engineer custom Model Context Protocol (MCP) servers, multi-model content pipelines, and AI operational systems. Explore our Claude AI Team Implementation Services or check out our complete Restoration Operations & AI Kit.

  • Claude AI Updates (June 2026): Fable 5 & Features

    Claude AI Updates (June 2026): Fable 5 & Features

    Last verified: August 26, 2026 (Pacific Time)May 2026 edition covered the Opus 4.8 launch, the SpaceX compute deal, and Managed Agents memory features.

    Direct Answer (August 2026 Update): Major mid-2026 Anthropic releases introduced Claude Fable 5, native Model Context Protocol (MCP) tool integration in Claude Desktop, extended 1M context beta on Vertex AI/AWS Bedrock, and decoupled Enterprise seat pricing.

    June 2026 is one of the biggest months for Anthropic since the Claude 4 launch: a new top-tier model is generally available, two workhorse models retire in four days, and Managed Agents can now run inside infrastructure you control. Here is everything that changed, with dates and migration paths.

    Claude Fable 5 — the Mythos-class model goes public (June 9, 2026)

    Abstract milestone timeline from early Claude eras through today without version numbers
    Fable 5 launch — keep the timeline abstract.

    Anthropic released Claude Fable 5 on June 9, 2026 — the public version of what had been known as its Mythos-class model tier. It is positioned as a new tier above Opus, and it is Anthropic’s most capable generally available model. According to CNBC’s launch coverage, Fable 5 scored more than 10% higher than Claude Opus 4.8 on some benchmarks, with exceptional performance across software engineering and knowledge work. Anthropic credits new safeguards that block responses in specific high-risk areas for making a broad release possible.

    The practical details developers need:

    • Model ID: claude-fable-5
    • Availability: enterprise customers and paid subscribers
    • Context window: 1 million tokens; maximum output 128K tokens
    • API pricing: $10 per million input tokens / $50 per million output tokens
    • API surface: adaptive thinking only — temperature, top_p, top_k, and budget_tokens are not accepted, and unlike Opus 4.8, an explicit thinking: {type: "disabled"} returns a 400 error. Omit the thinking parameter entirely if you do not want it.

    For where Fable 5 sits against every other Claude model on price, see our continuously updated Claude AI pricing guide, and our complete Fable 5 guide for capabilities and use cases.

    June 15 deadline: Claude Opus 4 and Sonnet 4 retire in four days

    If you are still calling claude-opus-4-20250514 or claude-sonnet-4-20250514, those models retire from the Claude API on June 15, 2026. Requests after retirement return 404 errors. The drop-in replacements:

    • claude-opus-4-20250514 → claude-opus-4-8
    • claude-sonnet-4-20250514 → claude-sonnet-4-6

    Note that both replacements use adaptive thinking rather than manual thinking budgets, and the 4.6+ models reject assistant-turn prefills — so this is a small migration, not just a string swap. Anthropic also deprecated Claude Opus 4.1 this month, with API retirement scheduled for August 5, 2026 — worth adding to your migration calendar now.

    Current Claude model lineup and API pricing (June 2026)

    Three cards for fast volume, daily workhorse, and deep flagship Claude seats
    Current lineup by job — no sticky version stickers on art.
    ModelModel IDContextMax outputInput $/1MOutput $/1M
    Claude Fable 5claude-fable-51M128K$10.00$50.00
    Claude Opus 4.8claude-opus-4-81M128K$5.00$25.00
    Claude Sonnet 4.6claude-sonnet-4-61M64K$3.00$15.00
    Claude Haiku 4.5claude-haiku-4-5200K64K$1.00$5.00

    Opus 4.7, 4.6, 4.5, and 4.1 and Sonnet 4.5 remain active for pinned workloads. We track which model is current at any moment in our current Claude model version reference.

    Managed Agents: self-hosted sandboxes and private MCP servers

    Three stacked layers: chat UI, tools, agent runtime
    Managed Agents and private MCP servers.

    Claude Managed Agents — Anthropic’s server-managed agent platform — can now execute tools inside a sandbox you control. The agent loop still runs on Anthropic’s orchestration layer, but bash commands, file operations, and code execution happen in your own container, behind your own firewall, with your own egress rules. Your worker long-polls Anthropic’s work queue over outbound-only connections; Anthropic never dials into your network. Managed Agents can also now connect to private MCP servers, which matters for any organization whose internal tools are not on the public internet.

    For regulated industries — healthcare, finance, legal — this is the missing piece that lets you adopt hosted agents while keeping data residency: files and tool output never leave infrastructure you own.

    Claude Code: nested sub-agents and plugin search

    Claude Code shipped a steady stream of updates in June: nested sub-agents (agents can now spawn their own sub-agents for deeper task decomposition), smarter model and region handling, a new plugin search, and improved Chrome, VS Code, and terminal workflows.

    Legal expansion: 20+ MCP connectors and 12 practice-area plugins

    Anthropic released more than 20 new legal MCP connectors and 12 practice-area plugins, covering research, contracts, discovery, matter management, and legal aid. The pattern to note: Anthropic is increasingly shipping vertical integration bundles rather than leaving connector-building entirely to the ecosystem.

    Claude Corps: $150M for nonprofit AI adoption

    Anthropic announced Claude Corps, a $150 million fellowship program that will embed roughly 1,000 trained fellows inside nonprofit organizations for a year to help them use AI effectively. Applications and program details are rolling out through Anthropic’s newsroom.

    Apple Foundation Models integration

    Claude support is coming to Apple’s Foundation Models framework on iOS 27, iPadOS 27, macOS 27, and visionOS 27 — meaning third-party Apple developers will be able to call Claude through Apple’s native AI framework rather than integrating the API directly.

    What to watch for in July

    • August 5, 2026: Claude Opus 4.1 retires from the API — migrate to claude-opus-4-8 before then.
    • Fable 5 ecosystem: expect Claude Code, Cowork, and Managed Agents to expose Fable 5 more broadly through July as capacity scales.
    • Apple rollout: developer betas of the iOS 27 family will show what Claude-via-Foundation-Models actually looks like in practice.

    Sources

    💼 Deploying Claude or AI Infrastructure in Your Business?

    At Tygart Media, we engineer custom Model Context Protocol (MCP) servers, multi-model content pipelines, and AI operational systems. Explore our Claude AI Team Implementation Services or check out our complete Restoration Operations & AI Kit.

  • Claude Fable 5 Complete Guide

    Claude Fable 5 Complete Guide

    New in 2026

    Everything you need to know about Anthropic’s new frontier tier — pricing, context window, model comparisons, and how to route the right work to the right model.

    Updated June 2026 · ~14 min read · Includes interactive calculators

    What Is Claude Fable 5?

    Three cards for fast volume, daily workhorse, and deep flagship Claude seats
    What is Claude Fable 5?

    Claude Fable 5 is Anthropic’s new frontier model tier — positioned above Opus in the lineup and designed for tasks where raw capability, extended reasoning depth, and massive context handling matter more than cost. Where Opus 4.8 set the bar for complex multi-step reasoning, Fable 5 raises it with a 1-million-token context window, enhanced agentic autonomy, and improved performance on long-horizon software engineering, research synthesis, and cross-domain analysis tasks.

    The “Fable” naming signals a new generation of model architecture rather than an incremental update. Anthropic positions it as the model you reach for when a task exceeds what Opus can do reliably — not as a replacement for Opus, Sonnet, or Haiku in their respective cost tiers.

    Quick Facts — Claude Fable 5

    Context Window
    1M
    tokens (~750K words)
    Max Output
    32K
    tokens per response
    Input Price
    $10
    per million tokens
    Output Price
    $50
    per million tokens
    Cache Write
    $12.50
    per million tokens
    Cache Read
    $1.00
    per million tokens
    Key positioning: Fable 5 is the model for tasks where Opus 4.8 produces reliable but imperfect results — long codebase audits, full-document analysis, complex multi-agent orchestration, and strategic synthesis across large corpora. For most production workflows, Sonnet remains the value pick.

    Full Model Lineup Comparison

    Pyramid diagram of Claude tiers: fast volume base, production workhorse middle, deep flagship peak
    Full model lineup comparison.

    Here’s how the complete 2026 Claude lineup stacks up across every dimension that matters for production usage:

    Model Input $/M Output $/M Context Max Out Vision Tool Use Extended Think Best For
    ◆ Fable 5 $10 $50 1M 32K ✓ ✓ ✓ Deep Max-capability tasks, 1M+ context
    ◆ Opus 4.8 $5 $25 200K 32K ✓ ✓ ✓ Complex reasoning, agentic workflows
    ◆ Sonnet 4.6 $3 $15 200K 16K ✓ ✓ ✓ Production apps, content at scale
    ◆ Haiku 4.5 $1 $5 200K 8K ✓ ✓ — High-volume, latency-sensitive tasks

    Prices are per million tokens. Cache read is 90% cheaper than standard input across all models. Batch API provides an additional 50% discount on both input and output.

    Capability Matrix — What Each Model Can Do

    Capability Fable 5 Opus 4.8 Sonnet 4.6 Haiku 4.5
    Full codebase analysis (>500K tokens)✓ Native⚠ Chunked✗✗
    Extended thinking / chain-of-thought✓ Deep✓✓✗
    Multi-step agentic orchestration✓ Best✓GoodLimited
    Computer use✓✓✓✗
    MCP tool integration✓✓✓✓
    Prompt caching✓✓✓✓
    Batch API (50% discount)✓✓✓✓
    PDF / document analysis✓✓✓Limited
    Real-time streaming✓✓✓✓
    Structured JSON output✓✓✓✓

    Interactive Cost Calculator

    Estimate your monthly API spend across the full model lineup. Enter your token volumes below — the calculator models prompt caching and Batch API discounts automatically.

    Token Cost Calculator

    Estimated Monthly Cost
    $0.00

    Which Claude Model Should You Use?

    Decision diagram from task shape to deep reasoning, daily shipping, or high-volume cheap calls
    Which Claude model should you use?

    Answer three questions to get a model recommendation tailored to your use case.

    Model Picker — 3 Questions
    1. How large is your context? (document/codebase size)
    Under 50K tokens
    50K–200K tokens
    200K–1M tokens
    2. How complex is the task?
    Simple / structured (classify, extract, format)
    Moderate (draft, summarize, QA)
    Complex (reason, plan, code, orchestrate)
    3. How cost-sensitive is this workload?
    Very — high volume, every cent counts
    Moderate — quality matters more than cost
    Not sensitive — quality and capability first

    How We Actually Use Each Model

    These are real production workflows mapped to the right tier — built from running Claude in content operations, publishing automation, and knowledge management at scale. No hypotheticals.

    Haiku 4.5 — High Volume
    Daily SEO Refresh Pipeline
    • 25-post-per-day SEO metadata refresh
    • Article classification and tag assignment
    • Structured data extraction from web pages
    • Keyword density checks across large post archives
    • Link validation and redirect flagging
    Sonnet 4.6 — Production Default
    Editorial Content at Scale
    • Desk article writing (1,200–2,500 words)
    • Content brief execution from keyword clusters
    • FAQ and schema markup generation
    • Cross-site content adaptation and localization
    • Monthly client update drafts and summaries
    Opus 4.8 — Complex Reasoning
    Workers & Deep Refreshes
    • Agentic Notion Workers (multi-step pipelines)
    • Deep content refresh with competitive gap analysis
    • Multi-database synthesis and reporting
    • Strategy documents requiring extended reasoning
    • Code generation for automation scripts
    Fable 5 — Max Capability
    Portfolio Audits & Strategy
    • Full-site content audits (500+ posts in single context)
    • Cross-domain strategy synthesis across large corpora
    • Complex multi-agent orchestration at the flagship tier
    • Long-horizon planning requiring deep reasoning depth
    • Codebase-wide analysis and architecture review
    Routing principle: The right model is the cheapest one that reliably completes the task. Haiku handles volume. Sonnet handles production. Opus handles complexity. Fable 5 handles scale + complexity together — specifically the cases where you’d need Opus and more context than Opus can hold.

    The Economics: Routed vs All-Fable

    Smart model routing is where API costs get controlled. Here’s a real-world comparison of a mixed content-and-automation workload at scale — routed vs running everything on Fable 5.

    Workload Monthly Volume Routed Model Routed Cost All-Fable 5 Cost Savings
    SEO metadata batch refresh750 posts/moHaiku 4.5 + Batch$1.20$18.7593% less
    Article drafting90 articles/moSonnet 4.6$8.10$67.5088% less
    Agentic worker runs200 runs/moOpus 4.8$22.50$45.0050% less
    Full-site portfolio audits4 audits/moFable 5$24.00$24.00—
    Total—Routed$55.80$155.2564% less

    Stacking Discounts: Caching + Batch API

    Two discount mechanisms compound independently:

    • Prompt caching: Cache your system prompt and shared context once. Subsequent requests pay ~10% of the input price for cache reads. On Fable 5, that’s $1.00/M instead of $10.00/M on cached tokens — a 90% reduction on your largest cost lever.
    • Batch API: Submit requests asynchronously (results within 24 hours) for a flat 50% discount on both input and output. Works on all four models. Best for non-real-time workloads like overnight refreshes, audits, or bulk classification.
    • Stacked: Caching + Batch combined can bring effective Fable 5 input cost from $10/M to ~$0.50/M on cached tokens — making it economically viable for high-volume tasks that previously only fit Haiku’s budget.

    See our Claude context window guide for more on how to structure prompts to maximize cache hit rates.

    Related Claude Resources

    Claude Context Window Guide Claude Projects Deep Dive Claude Code Plan Mode Claude MCP Setup Guide Opus 4.7 Feature Deep Dive Claude Code vs Cursor (2026)

    Claude Fable 5 FAQ

    Claude Fable 5 sits above Opus 4.8 in the lineup. The primary difference is context window size — Fable 5 offers 1 million tokens vs Opus 4.8’s 200K — and the depth of extended reasoning for highly complex tasks. Opus 4.8 remains the right choice for most complex agentic workflows at half the cost. Fable 5 is best when you need both maximum context and maximum reasoning depth simultaneously, or when a task has routinely hit the limits of what Opus can do reliably.
    Claude Fable 5 is priced at $10 per million input tokens and $50 per million output tokens — 2× Opus 4.8 ($5/$25), 3.3× Sonnet 4.6 ($3/$15), and 10× Haiku 4.5 ($1/$5). Prompt caching drops the effective input cost to $1.00/M on cache reads, and the Batch API adds a 50% discount on all tokens for non-real-time workloads. Stacking both discounts makes Fable 5 viable for higher-volume use cases than the base price suggests.
    Claude Fable 5 has a 1-million-token context window — approximately 750,000 words or roughly 1,500 pages of text. This is 5× the context window of Opus 4.8, Sonnet 4.6, and Haiku 4.5 (all 200K). In practice, a 1M context window lets you pass entire codebases, long research corpora, or full document archives in a single API call without chunking or retrieval workarounds. For more on context window mechanics, see our full context window guide.
    Yes. Claude Fable 5 is available through the Anthropic API using the model ID claude-fable-5-20260101 (check the Anthropic documentation for the exact identifier). It supports the same API surface as the rest of the Claude family — streaming, tool use, prompt caching, vision, the Batch API, and MCP server integration. Access requires an Anthropic API account with Fable 5 enabled on your usage tier.
    Fable 5 is available in Claude.ai on the Pro and Team plans. The interface lets you select it from the model picker when starting a conversation. Like Opus, Fable 5 in claude.ai has message limits that reset on a rolling window — it’s designed for individual complex tasks rather than high-volume API workloads. For production-scale usage, the API with the Batch API discount is the more economical path.
    Yes — and Fable 5’s extended thinking is the deepest in the lineup. Where Opus 4.8 supports extended thinking for complex reasoning tasks, Fable 5 uses a more capable reasoning engine designed for tasks that require longer chains of inference, more working memory, and more reliable self-correction. It’s particularly effective on math, logic, long-horizon planning, and tasks where the model needs to hold and manipulate many interdependent concepts simultaneously.
    For most content production — articles, blog posts, social copy, summaries, SEO content — Sonnet 4.6 is the right call. It produces high-quality output at 3.3× less cost than Fable 5, and for typical content lengths (500–3,000 words), the quality difference is minimal. Reach for Fable 5 when you need to synthesize across a very large corpus (e.g., auditing 200+ posts simultaneously), when the content requires deep domain reasoning that benefits from extended thinking, or when the task involves both large-context ingestion and complex output generation in a single pass.
    Three levers in order of impact: (1) Model routing — only use Fable 5 when the task genuinely requires it; route everything else to Opus, Sonnet, or Haiku based on complexity and volume. (2) Prompt caching — structure your system prompt and shared context so it can be cached; cache reads cost $1.00/M instead of $10.00/M on Fable 5. (3) Batch API — submit non-real-time workloads via the Batch API for a flat 50% discount. Stacking all three — routing + caching + batch — can reduce effective per-task costs by 85–95% compared to unoptimized Fable 5 calls.

    More Claude Guides from Tygart Media

    We run Claude in production every day. These are the guides that come from using it, not just writing about it.

    Context Window Guide Claude Projects Plan Mode MCP Setup Code vs Cursor

    Related on Tygart Media: Claude Fable 5 overview · Fable 5 firsthand · Claude pricing.

  • Connect Claude Code to Postgres via MCP: The Right Way (2026)

    Connect Claude Code to Postgres via MCP: The Right Way (2026)

    The most useful thing you can wire into Claude Code isn’t a new model or a clever prompt — it’s your actual database. When Claude Code can read your schema, it stops guessing at table names, column types, and relationships. It starts writing queries that work the first time.

    This is the practical walkthrough for connecting Claude Code to a Postgres database via MCP: the command, the credential setup, the safety pattern, and what the workflow actually looks like once it’s running.

    What the Postgres MCP server does

    Flow from app/IDE through MCP to servers and data APIs
    What the Postgres MCP server does.

    The official @modelcontextprotocol/server-postgres package (maintained in the MCP reference implementations repo) gives Claude Code four tools: schema inspection, query execution inside a read-only transaction, table detail lookup, and relationship traversal. The server cannot write data — it’s read-only by design in the reference implementation, though third-party variants like postgres-mcp-pro add configurable write access if you need it.

    For the majority of development workflows — debugging, writing migrations, generating queries — read-only is exactly what you want. Claude Code can see the shape of your data without being able to touch it.

    The fastest path: single command setup

    Side-by-side cards defining what Claude Code is and is not
    Fastest path — single command setup.

    If you just want it running against a local database:

    claude mcp add postgres -- npx -y @modelcontextprotocol/server-postgres "postgresql://USER:PASSWORD@localhost:5432/mydb"

    The -y flag on npx auto-accepts the package install so the command doesn’t hang on first run. Verify with:

    claude mcp list

    You should see postgres with a connected status. That’s it — Claude Code now has schema access in the current project.

    Don’t do this for a production database. The connection string above is hardcoded. It goes into ~/.claude.json as plaintext. Use a dedicated local or staging database during development, and use env vars for anything that matters.

    The right way: env vars and a read-only user

    Five security domains: identity, data, code governance, audit, agents
    Right way — env vars and a read-only user.

    Two things to do before connecting to any real database:

    1. Create a read-only Postgres user:

    CREATE USER claude_readonly WITH PASSWORD 'your-password-here';
GRANT CONNECT ON DATABASE your_db TO claude_readonly;
GRANT USAGE ON SCHEMA public TO claude_readonly;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO claude_readonly;
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO claude_readonly;

    This user can see everything in public and do nothing else. If something goes wrong — a rogue tool call, a compromised session — blast radius is zero.

    2. Reference the connection string via env var in .mcp.json:

    {
  "mcpServers": {
    "db": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres"],
      "env": {
        "POSTGRES_CONNECTION_STRING": "${DATABASE_URL}"
      }
    }
  }
}

    Set DATABASE_URL in your shell environment or in a .env file (not committed). Add .mcp.json to the repo — everyone on the team gets the same server config — but the actual connection string lives in each developer’s local environment. This is the same pattern you already use for application config. It’s the right pattern here too.

    Add the server at project scope so it’s committed:

    claude mcp add --scope project --transport stdio db -- npx -y @modelcontextprotocol/server-postgres

    Then edit .mcp.json to replace the hardcoded connection string with the ${DATABASE_URL} env var reference shown above.

    What Claude Code can actually do with schema access

    Once the server is connected, the workflow changes significantly. A few real examples:

    Schema exploration: Ask Claude Code “what tables are in this database and how are they related?” and it traverses foreign keys, describes join paths, and builds a mental model of your data layer. No more copy-pasting \dt output.

    Query generation: “Write a query that finds users who signed up in the last 30 days but haven’t completed onboarding” produces accurate SQL because Claude Code knows your actual column names. With a generic prompt and no schema access, you’d get plausible-looking SQL that fails because user_status is actually onboarding_state.

    Migration drafting: “I need to add a last_login_at column to users — show me the migration and check for existing timestamp patterns in the schema.” Claude Code inspects the schema first, matches your existing column naming conventions, and produces a migration that fits your codebase.

    Debugging: “This query is returning the wrong count — here’s the query, check it against the schema.” Claude Code can spot that you’re joining on a nullable column, missing a filter on a soft-delete flag, or aggregating before filtering.

    Neon and cloud Postgres

    If you’re on Neon, there’s a first-party MCP server with additional capabilities: branch management, database creation, and schema migrations via Neon’s branching model. Set it up with:

    npx neonctl mcp add

    This runs OAuth through your browser and configures Claude Code automatically. The Neon MCP server is intended for local development and IDE workflows — not production automation — same caution applies.

    Debugging when it doesn’t connect

    Three commands for when the server shows as disconnected:

    claude mcp list          # check registered servers and status
claude mcp test db       # test a specific server
claude --debug           # tail logs including MCP stderr output

    Most connection failures are either a wrong connection string, a missing env var, or Node version issues with npx. The debug log shows the exact error from the server process — read it before assuming the problem is Claude Code.

    The practical baseline

    Five minutes to set up. The productivity delta on any codebase larger than a few tables is immediate — Claude Code stops making column-name mistakes and starts being genuinely useful for data-layer work. Wire up the read-only user, commit the .mcp.json, and add DATABASE_URL to your team’s .env.example. Done.

    The model doing the work in a typical Claude Code session is claude-sonnet-4-6 (workhorse) — it handles schema-aware query generation well without burning through Opus 4.8 credits on every lookup.

    Related Reading

    Frequently Asked Questions

    How do I connect Claude Code to a Postgres database via MCP?

    Run: claude mcp add postgres — npx -y @modelcontextprotocol/server-postgres “postgresql://USER:PASSWORD@localhost:5432/mydb”. The -y flag auto-accepts the package install so the command doesn’t hang. Then run claude mcp list and confirm postgres shows a connected status. Use a local or staging database for this quick path, not production.

    Is the Postgres MCP server read-only?

    Yes. The official @modelcontextprotocol/server-postgres package is read-only by design — it exposes schema inspection, query execution inside a read-only transaction, table detail lookup, and relationship traversal. It cannot write data. Third-party variants like postgres-mcp-pro add configurable write access if you need it.

    What’s the safe way to connect Claude Code to a production database?

    Create a dedicated read-only Postgres user (GRANT SELECT only), and reference the connection string through an environment variable in .mcp.json using ${DATABASE_URL} rather than hardcoding it. Commit .mcp.json so the team shares the server config, but keep the actual connection string in each developer’s local .env. If a session is ever compromised, the blast radius is zero.

    Why does schema access make Claude Code more accurate?

    With schema access, Claude Code reads your real table names, column types, and relationships, so it writes queries that work the first time instead of guessing. Without it, you get plausible-looking SQL that fails because user_status is actually onboarding_state. It also improves migration drafting and query debugging by matching your existing conventions.

    How do I debug a Postgres MCP server that won’t connect?

    Use three commands: claude mcp list to check registered servers and status, claude mcp test db to test a specific server, and claude –debug to tail logs including MCP stderr. Most failures are a wrong connection string, a missing env var, or a Node version issue with npx — the debug log shows the exact server error.