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.

[CI] [Security Audit] [CodeQL] [OpenSSF Scorecard] [python-docs-mcp-server MCP server] [MCP Registry] [License: MIT] [Python 3.12+] [No API Keys] [Official Python Docs]

[Python Docs MCP server]

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
• seven 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.

Search result behavior

For kind="auto" with an explicit version, a nonempty search with spare result slots may append one exact inventory target when the prompt contains one known qualified identifier in that version. Original hits, snippets, scores, order and notes are preserved. Full results, ambiguous identifiers, other kinds and versionless queries are unchanged; the existing empty-result fallback is unchanged. Canonical locations are deduplicated, including inventory aliases.

The append is admitted only when the complete proposed response serialized by Pydantic's compact model_dump_json() is at most 8000 UTF-8 bytes. This is not a global search cap: oversized original responses remain unchanged. It is separate from get_docs(max_chars=8000), which limits content characters.

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.

Python 3.15 documentation (opt-in)

The source-pinned documentation targets include Python 3.10–3.15. To index 3.15 alongside 3.14, explicitly run:

uvx python-docs-mcp-server build-index --versions 3.14,3.15

A build replaces the local index with the requested versions; include every version you want to keep. The first-run examples above intentionally remain unchanged. The builder still marks the highest requested version as the index default; adding this target does not select 3.15 in an existing index. get_docs without a version uses the configured/detected default, while versionless search spans indexed versions. Pass version="3.15" for isolated 3.15 answers. list_versions reports the actual index, not all build targets; an unindexed explicit version raises an error, never an older-docs fallback.

This is documentation coverage, not a claim that the server runs on Python 3.15 or that a named MCP client was tested with it. Queries remain offline and read-only after a successful build. Until it is indexed, consult the official 3.15 docs directly.

The 3.15 target is CPython tag v3.15.0, commit d220409a13b77de7a6c30abe16ea90db01528edc, with Sphinx 8.2.3. See integration checks for explicit-version and comparison examples.

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 seven MCP tools:

For search_docs with kind="auto" and an explicit indexed version, the original lookup and full-text search run first. If both return no hits, the server looks for dotted Python identifiers in the prompt. Exactly one distinct identifier registered in that version enables a symbol-lookup fallback; repeated mentions count once and unknown identifiers do not count. Multiple known identifiers leave the result empty. A result note identifies fallback matches, which are symbol documentation, not an answer to the complete prompt. Explicit kinds, searches without a version, nonempty results, and result budgets are unchanged.

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.

Air-gapped install

The server needs no network at query time. To install it on a machine with no network:

1. On a connected machine with the same OS, CPU architecture and Python minor version, download the wheels and build the index. Elsewhere, add pip's --platform, --python-version and --only-binary=:all: options to pip download.

   bash

   pip download python-docs-mcp-server==<version> -d wheels/pip install python-docs-mcp-server==<version>python-docs-mcp-server build-index --versions 3.10,3.11,3.12,3.13,3.14

2. Copy wheels/ and the built index.db to the target machine.

3. On the target, install from the copied wheels only:

   bash

   pip install --no-index --find-links wheels/ python-docs-mcp-server==<version>

4. Put index.db at ~/Library/Caches/mcp-python-docs/index.db on macOS, or at ~/.cache/mcp-python-docs/index.db on Linux ($XDG_CACHE_HOME/mcp-python-docs/index.db when XDG_CACHE_HOME is set).

5. Set PYTHON_DOCS_MCP_DISABLE_AUTO_INDEX=1, so a missing index never starts a network build, and check the install:

   bash

   PYTHON_DOCS_MCP_DISABLE_AUTO_INDEX=1 python-docs-mcp-server doctor

lookup_package_docs needs PyPI. Offline, it returns a clear note instead of package sources. There is no separate offline command.

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, the index format, disk space, and the venv/ensurepip support needed by build-index. If it reports FAIL: Index format, run the build-index command it prints; it lists the versions your index already holds (or every supported version, if it cannot read them). If it says your index is newer than this release, upgrade python-docs-mcp-server instead.

Validate an existing index:

uvx python-docs-mcp-server validate-corpus

Troubleshooting

FTS5 unavailable

The index needs SQLite's FTS5 extension. The python.org installers, uv python install, and the Pythons from Linux distributions, Homebrew and conda all include it. If doctor reports FAIL: SQLite FTS5, your Python was built against a SQLite without FTS5. Install one that has it:

uv python install

Or install Python from python.org.

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. Source-pinned documentation targets cover Python 3.10 through 3.15; Python 3.15 is opt-in. The versions available for queries depend on the documentation versions selected when building the index.

License

MIT