tenequm

LLMs Txt MCP — Documentation skill for Claude Code

Documentation community

Lean MCP server for fast llms.txt documentation access.

How to install LLMs Txt MCP

This entry records only its repository, not the path inside it, so there is no exact command to give. Open tenequm/llms-txt-mcp and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What LLMs Txt MCP does

Lean MCP server for fast llms.txt documentation access.

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README

llms-txt-mcp

[![llms-txt-mcp](https://img.shields.io/pypi/v/llms-txt-mcp?label=llms-txt-mcp)](https://pypi.org/project/llms-txt-mcp/) [![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/) [![MCP SDK 1.12+](https://img.shields.io/badge/MCP%20SDK-1.12+-purple.svg)](https://github.com/modelcontextprotocol/python-sdk) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

Fast, surgical access to big docs in Claude Code via llms.txt. Search first, fetch only what matters.

Why this exists

  • Hitting token limits and timeouts on huge llms.txt files hurts flow and drowns context.
  • This MCP keeps responses tiny and relevant. No dumps, no noise — just the parts you asked for.

Quick start (Claude Desktop)

Add to `~/Library/Application Support/Claude/claude_desktop_config.json` or `.mcp.json` in your project:

{
  "mcpServers": {
    "llms-txt-mcp": {
      "command": "uvx",
      "args": [
        "llms-txt-mcp",
        "https://ai-sdk.dev/llms.txt",
        "https://nextjs.org/docs/llms.txt",
        "https://orm.drizzle.team/llms.txt"
      ]
    }
  }
}

Now Claude Code|Desktop can instantly search and retrieve exactly what it needs from those docs.

How it works

URL → Parse YAML/Markdown → Embed → Search → Get Section

  • Parses multiple llms.txt formats (YAML frontmatter + Markdown)
  • Embeds sections and searches semantically
  • Retrieves only the top matches with a byte cap (default: 75KB)

Features

  • Instant startup with lazy loading and background indexing
  • Search-first; no full-document dumps
  • Byte-capped responses to protect context windows
  • Human-readable IDs (e.g. https://ai-sdk.dev/llms.txt#rag-agent)

Source resolution and crawling behavior

  • Always checks for llms-full.txt first, even when llms.txt is configured. If present, it uses llms-full.txt for richer structure.
  • For a plain llms.txt that only lists links, it indexes those links i