Python Docs MCP Server

python-docs-mcp-server

For AI coding agents writing Python, python-docs-mcp-server is the canonical Python stdlib oracle: exact symbols, exact sections, exact versions — offline, always free, always MIT, token-frugal.

Built for the moment your agent needs asyncio.TaskGroup signatures, pathlib.Path semantics, or what changed in 3.12 — not a web fetch, not a hosted API, not a vector store hallucinating section anchors. Just an indexed slice of docs.python.org, returned by symbol or by query, scoped to the version you actually ship on.

Why this exists

There is a difference between long context and usable context. Research like the Lost in the Middle study (Liu et al., 2023) found that models struggle to use relevant information buried in the middle of a long input. Pasting an entire documentation page into the model invites that failure. Returning the exact symbol, section, and version keeps the answer in a short, high-signal context instead.

Generic docs retrieval is a rough fit for Python stdlib questions:

• asyncio.TaskGroup should resolve to the actual symbol, not a fuzzy page hit
• Python version matters (3.12 and 3.13 do not always say the same thing)
• fetching a whole page burns tokens when one section answers the question
• the official docs are canonical, but they do not ship as an MCP server

This server indexes the official docs locally and exposes a small set of MCP tools for lookup and section retrieval.

Why use it

• no API keys to manage
• queries run against a local index, not a hosted docs API
• results come from the official Python docs
• the server is read-only at runtime
• fewer dependencies to review in strict environments

What you get

• symbol lookup through Python objects.inv
• page and section retrieval with truncation and pagination
• a local SQLite + FTS5 index; no runtime web scraping
• results for each Python version you index
• six read-only MCP tools

Quick example

Prompt

What does asyncio.TaskGroup do in Python 3.13?

Typical flow

1. search_docs("asyncio.TaskGroup", kind="symbol", version="3.13")
2. Call get_docs(...) using the slug and anchor returned by the best hit

Result

The model gets the matching symbol and the relevant docs section, not a full-page dump.

30-second demo

Ask your MCP client:

In Python 3.13, how should I use asyncio.TaskGroup and what changed from older asyncio patterns?

If setup is working, the client should use search_docs for the exact symbol, then get_docs for the matching section. Instead of generic web results or an entire docs page, it gets official stdlib text for the requested Python version, trimmed to the section that matters.

Install

Run directly with uvx:

uvx python-docs-mcp-server --version

Or install it once with pipx:

pipx install python-docs-mcp-server

---

If uv is installed but the uv command is not on your PATH, reopen your shell or use python -m uv ... as a fallback for local contributor commands.

First run

Build the local documentation index:

uvx python-docs-mcp-server build-index --versions 3.10,3.11,3.12,3.13,3.14

If you installed the package persistently, you can drop the uvx prefix:

python-docs-mcp-server build-index --versions 3.10,3.11,3.12,3.13,3.14

The first build downloads Python's objects.inv files, clones CPython docs sources, runs sphinx-build -b json, and writes an SQLite index to your local cache. It can take several minutes.

Configure your MCP client

Claude Code

This repository includes a project-scoped .mcp.json for clients that support checked-in MCP server configuration. It points at the published package:

{
  "mcpServers": {
    "python-docs": {
      "type": "stdio",
      "command": "uvx",
      "args": ["python-docs-mcp-server"]
    }
  }
}

Build the local documentation index with the first-run command above before expecting docs queries to return corpus-backed results.

Claude Desktop

Add this to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Linux: ~/.config/Claude/claude_desktop_config.json

Windows: %APPDATA%\\Claude\\claude_desktop_config.json

{
  "mcpServers": {
    "python-docs": {
      "command": "uvx",
      "args": ["python-docs-mcp-server"]
    }
  }
}

Restart Claude Desktop after editing the config file.

Cursor

Add this to your Cursor MCP settings (.cursor/mcp.json in your project or global settings):

{
  "mcpServers": {
    "python-docs": {
      "command": "uvx",
      "args": ["python-docs-mcp-server"]
    }
  }
}

Codex

Add this to .codex/config.toml:

[mcp_servers.python-docs]
command = "uvx"
args = ["python-docs-mcp-server"]

Quality checks

• CI runs ruff, pyright, and pytest on macOS and Linux for Python 3.12 and 3.13
• subprocess-based stdio and smoke tests cover the MCP protocol pipe
• packaging tests check the wheel contents and CLI entry points
• retrieval regression tests cover exact symbol hits, version behavior, missing symbols, truncation, and local-version defaults
• manual MCP QA lives in .github/INTEGRATION-TEST.md, with MCP Inspector for local checks and Claude/Cursor for real-client checks

Contributor commands and validation steps live in CONTRIBUTING.md.

Tools

The server currently exposes six MCP tools:

Why not Context7 or generic docs retrieval?

Use this server when you want precise local Python docs retrieval rather than broad web search:

• official Python docs, not scraped mirrors or summaries
• exact symbol resolution from objects.inv
• version-aware results for Python 3.10 through 3.14
• section retrieval instead of full-page dumps
• PyPI-declared docs, homepage, and source links through lookup_package_docs
• local read-only runtime with no API keys

Use Context7 or a generic docs fetcher for third-party libraries, arbitrary web pages, or framework research. This server is not a universal docs search engine; it is a focused stdlib retrieval tool for AI coding agents.

Retrieved docs cache

get_docs responses are cached across MCP client/server restarts in the platform cache directory:

<platform cache dir>/mcp-python-docs/retrieved-docs-cache.sqlite3

The cache stores completed get_docs results for the resolved Python docs version plus request identity (slug, optional anchor, max_chars, and start_index). Cache misses use the normal local index retrieval path and then write the result.

Cache entries are also scoped to a fingerprint of the local index.db file (path, size, and modification timestamp). If you rebuild or replace the local docs index, older entries are ignored automatically. Deleting retrieved-docs-cache.sqlite3 is safe; it removes cached retrieval results, not the docs index.

PyPI package docs lookup

lookup_package_docs queries the official PyPI JSON API documented at https://docs.pypi.org/api/json/ (GET /pypi/<project>/json) and returns only sources declared in that package's PyPI metadata: the PyPI project URL, docs_url, home_page, and allowlisted project_urls labels such as Documentation, Homepage, Source, and Repository.

The tool makes the trust boundary explicit with trust_boundary="pypi-declared-metadata". It does not crawl pages, perform web search, or silently fall back to unofficial community mirrors.

Diagnostics

Check the local environment:

uvx python-docs-mcp-server doctor

This checks the runtime Python version, SQLite FTS5, cache/index paths, disk space, and the venv/ensurepip support needed by build-index.

Validate an existing index:

uvx python-docs-mcp-server validate-corpus

Troubleshooting

FTS5 unavailable

If your Python build does not include SQLite FTS5:

Linux x86-64

Linux x86-64 users can install the optional bundled SQLite package:

pip install 'python-docs-mcp-server[pysqlite3]'

macOS / Windows / Linux ARM

Install Python from python.org or use:

uv python install

Missing pythonX.Y-venv on Debian/Ubuntu

If doctor says build venv support is unavailable, install the venv package for the same Python minor version that runs the server:

sudo apt install python3.12-venv

Adjust 3.12 to match the version shown by doctor. Without this package, build-index cannot create the disposable Sphinx environment it uses to build JSON documentation content.

Migrating from the pre-rename CLI

Earlier development snapshots of this project used the PyPI name mcp-server-python-docs. The published PyPI project is python-docs-mcp-server. If your MCP client config still references the old name via uvx, you will see a Package not found error, because uvx resolves projects by PyPI name.

Change your config args from:

"args": ["mcp-server-python-docs"]

to:

"args": ["python-docs-mcp-server"]

The wheel still installs a legacy mcp-server-python-docs console script for users who already have the package installed and invoke the binary by name on $PATH. That script is an alias and will be removed in a future release.

uvx cache stale

If uvx python-docs-mcp-server runs an old version:

uvx --reinstall python-docs-mcp-server

Or clear the uv cache:

uv cache clean python-docs-mcp-server

Claude Desktop on Windows (MSIX)

The MSIX-packaged version of Claude Desktop on Windows may have restricted PATH access. If uvx is not found, specify the full path in your config:

{
  "mcpServers": {
    "python-docs": {
      "command": "C:\\Users\\YOU\\.local\\bin\\uvx.exe",
      "args": ["python-docs-mcp-server"]
    }
  }
}

Replace YOU with your Windows username. Find the exact path with where uvx.

Restart after rebuild

After running build-index, restart your MCP client so it picks up the new database file. The server opens the index read-only on startup and does not reload it while running.

On Windows, close the MCP client before rebuilding if the index file is locked.

Contributor workflow

For contributor setup and verification:

• CONTRIBUTING.md
• .github/INTEGRATION-TEST.md
• .github/RELEASE.md

Support

Tested on macOS and Linux. Windows should work, but it is not verified on every release.

The server requires Python 3.12+ to run. Its generated documentation corpus covers Python documentation versions 3.10 through 3.14.

License

MIT