Llmstxt Doc Search MCP server

llmstxt-doc-search

Live, ranked search across any number of llms.txt documentation sites - Strands, Kiro, the AWS guides, and whatever you add at runtime.

llmstxt-doc-search is a Model Context Protocol (MCP) server that turns the llms.txt index a documentation site publishes into a fast, ranked search tool your agent can call. It indexes titles at startup, ranks queries with BM25, and fetches the full document only when you open a result - so you get current docs with almost no local storage. Built on the search engine from @praveenc/mcp-docs-server, generalized to a runtime registry of sources.

Why

An llms.txt file is a curated index of a doc site's pages, published for tools like this one to consume. They can be large - AWS Bedrock's lists roughly a thousand documents - so downloading everything is wasteful and goes stale fast.

This server takes a leaner approach:

• Title-only index, built lazily. On first search of a source, only the page titles are indexed. That is fast to build and tiny to hold in memory.
• Ranked with BM25. Queries are scored with BM25 plus Porter stemming, bigrams, and markdown-aware weighting (headers, code, and links count for more). Technical terms like mcp, json, and stdio are preserved rather than stemmed.
• Content on demand. The full markdown or HTML of a result is fetched only when you call fetch_doc.

The result is a good fit for broad, fast-moving reference material - the opposite tradeoff to snapshotting docs into a local vault.

Installation

Quick start (recommended)

Add the server to your MCP client configuration (Claude Desktop, Kiro, and others). It is downloaded and run on demand via npx - no manual build:

{
  "mcpServers": {
    "llmstxt-doc-search": {
      "command": "npx",
      "args": ["-y", "@praveenc/llmstxt-doc-search"]
    }
  }
}

Global install

npm install -g @praveenc/llmstxt-doc-search

Then point your MCP client at the installed binary:

{
  "mcpServers": {
    "llmstxt-doc-search": {
      "command": "llmstxt-doc-search"
    }
  }
}

Quick start

Once the server is connected, the typical flow is three calls:

1. docshome()** - orient yourself: see the registered sources and how to search and fetch.
2. searchdocs("prompt caching", "aws-bedrock-userguide")** - rank matching docs. Omit the source to search everything.
3. fetchdoc(url)** - read the full content of a result you like.

Add your own source at any time and it is indexed immediately and persisted for future runs:

add_doc_source("langgraph", "https://langchain-ai.github.io/langgraph/llms.txt")

Tools

Shortened. The full README is on GitHub.