anydocs
Provides search and retrieval of OpenAI Codex documentation, enabling agents to find relevant pages and snippets via BM25 search and regex grep.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@anydocsSearch Claude Code docs for MCP tool usage"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
anydocs
An MCP server that gives coding agents fast search over other tools' documentation — Claude Code, OpenAI Codex, Cursor, opencode, xAI, and whatever else you add.
Docs are ingested in CI, indexed into SQLite FTS5, and published as a release artifact. The server downloads it and serves five tools:
tool | what it does |
| BM25-ranked hits as short snippets — never whole sections |
| one page, or one heading section of it |
| regex over the raw markdown, for exact symbols BM25 splits |
| which doc sets are indexed |
| a source's pages and descriptions |
A search costs ~500 tokens. Returning whole matched sections instead — the obvious way to build this — costs 10k+ for the same question. That gap is the reason anydocs exists.
Everything runs locally: no API key, no network at query time, no service to keep alive. The whole index is ~7 MB.
Install
Codex
Codex reads MCP servers from ~/.codex/config.toml or, for a trusted project,
.codex/config.toml. Add it globally with the CLI:
codex mcp add anydocs -- \
uvx --from git+https://github.com/kiyeonjeon21/anydocs anydocs
codex mcp listOr use project configuration. The longer startup timeout covers the first cold
uvx install and index download; required makes a broken server fail loudly.
[mcp_servers.anydocs]
command = "uvx"
args = [
"--from",
"git+https://github.com/kiyeonjeon21/anydocs",
"anydocs",
]
startup_timeout_sec = 120
required = true
[mcp_servers.anydocs.env]
ANYDOCS_SOURCES = "codex"Restart Codex after changing configuration. To make tool selection reliable,
put this in the project's AGENTS.md:
When anydocs MCP is available, use search_docs with the product's source and
then read_doc before answering questions about that product's documentation.Clients using .mcp.json
For clients that support .mcp.json, use the following. Nothing needs to be
installed first: uvx fetches the server, and the server fetches the index on
first run.
{
"mcpServers": {
"anydocs": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/kiyeonjeon21/anydocs",
"anydocs"
]
}
}
}Related MCP server: MCP Spark Documentation Server
Scoping a project to the docs it uses
ANYDOCS_SOURCES limits the server to the sources you name. The rest disappear —
from list_sources, from the source enum the model sees, and from every tool,
including direct read_doc calls.
Worth doing. These doc sets describe the same ideas in different words, so on a
Claude Code repo an unfiltered search for hook events hands 3 of its 5 slots to
Cursor and xAI.
{
"mcpServers": {
"anydocs": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/kiyeonjeon21/anydocs",
"anydocs"
],
"env": {
"ANYDOCS_SOURCES": "claude-code,codex"
}
}
}
}In Codex config, the equivalent is:
[mcp_servers.anydocs.env]
ANYDOCS_SOURCES = "claude-code,codex"Available: claude-code, codex, cursor, opencode, xai. A name that is not
in the index stops the server and prints the valid ones, rather than quietly
serving an empty index.
What it does not do
Matching is lexical, and the tools say so rather than bluffing:
English only. The docs are English and matching is by word, so a Korean or Japanese query reaches nothing.
search_docsnames the words it had to ignore instead of quietly answering a question you did not ask.No fuzzy matching. A typo finds nothing. It is reported as a typo.
OR matching always finds something. Ask Claude Code's docs about
cursorrulesand the hits will be pages that merely containtab. The reply says which of your words never reached the results, so a weak match cannot pass as an answer.
Embeddings were measured and left out: dense retrieval alone scored worse than BM25 on these corpora (hit@1 0.775 vs 0.804), and a hybrid moved recall@8 from 0.946 to 0.964 — five questions out of 276 — in exchange for a 130 MB model on every client or a server to keep running. Not worth it yet.
Adding a source
Drop a YAML file in sources/. Sites do not agree on how to publish docs, so
there are three ingest strategies:
strategy | when | example |
| llms.txt is an index of pages, each with a | Claude Code, Codex |
| no llms.txt — take the page list from sitemap.xml | Cursor, opencode |
| llms.txt is the corpus, split by a delimiter | xAI |
id: cursor
title: Cursor
tags: [coding-agent]
strategy: sitemap
entry: https://cursor.com/docs/sitemap.xml
base_url: https://cursor.com/docs/
page_suffix: .md
include: ["https://cursor.com/docs/*"] # the sitemap carries 13 locales
expect_pages: 165 # guards against the site movingTwo things to get right, both of which fail silently:
Locales. Every sitemap carries them, and they can multiply a source by 17.
expect_pagesis checked in both directions, so a filter that stops matching is a build failure rather than a quietly bloated index.slug_style. Sites slug their heading anchors differently, and a wrong slug still ranks fine — it just lands in the wrong place, which nothing else would catch.collapsefor Mintlify (CLAUDE.md→claude-md),githubfor Astro Starlight (Avante.nvim→avantenvim),verbatimfor the rest. CI checks every anchor against the live HTML on each sync.
CI re-ingests daily and publishes a new index only when the docs actually changed.
Development
uv run anydocs-build # ingest + index into build/
uv run pytest -q
uv run python scripts/eval_search.py # retrieval quality against a gold set
uv run python scripts/verify_anchors.py # anchors resolve on the live sites
uv run python scripts/sweep_chunk.py # re-chunk from pages.body, no refetchA local build/ directory takes precedence over the published index, so
anydocs-build then anydocs serves what you just built.
Retrieval changes need evidence. scripts/eval_search.py scores against a
hand-written gold set plus 276 auto-derived questions (each page's llms.txt
description, which is a paraphrase and is not among the indexed columns). A
one-case swing on the hand set is noise; several plausible improvements died on
these numbers.
License
MIT
Maintenance
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