MCP 服务器

LocalDocs MCP

Creates a local database of indexed technical documentation from web crawls and local files, enabling AI agents to efficiently search and retrieve documentation through MCP tools.

README

LocalDocs MCP

A Model Context Protocol (MCP) server that creates a local database of indexed and optimized technical documentation. It enables AI agents to efficiently query, search, and retrieve documentation from both web sources and local files through MCP tools.

Features

Web Crawling: Automatically crawl and index documentation websites
Local File Indexing: Process local markdown documentation
AI-Powered Processing: Optional AI enhancement for metadata extraction and example generation
Smart Search: Fuzzy search and semantic retrieval capabilities
Efficient Storage: Folder-based markdown storage with frontmatter metadata
MCP Integration: Full MCP protocol support for AI agent interaction
Async Architecture: Fast, concurrent processing throughout

Installation

# Install from source
git clone https://github.com/dylan-gluck/localdocs-mcp
cd localdocs-mcp
uv sync

# Run directly with uvx (coming soon)
# uvx localdocs-mcp

Quick Start

1. Initialize a Documentation Collection

# Crawl web documentation
localdocs init react --crawl https://react.dev/learn --depth 2

# Index local files
localdocs init myproject --local ~/Documents/myproject/docs

# With AI processing (requires OpenAI API key)
localdocs init vue --crawl https://vuejs.org/guide/ --ai

2. Search Documentation

# Search across all collections
localdocs search "useState hook"

# Search specific collection
localdocs search "component props" --collection react

# List all collections
localdocs list

3. Configure MCP Client

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "localdocs": {
      "command": "uvx",
      "args": ["localdocs-mcp", "serve"],
      "env": {
        "OPENAI_API_KEY": "${OPENAI_API_KEY}"  // Optional, for AI processing
      }
    }
  }
}

MCP Tools

The server exposes the following tools to AI agents:

Tool	Description	Parameters
`search_docs`	Search across all documentation	`query`, `collection?`, `limit?`
`list_collections`	List available collections	-
`get_document`	Get specific document by ID	`doc_id`
`list_examples`	List code examples	`collection?`, `language?`
`fuzzy_find`	Fuzzy search documents	`pattern`, `collection?`

CLI Commands

Collection Management

# Initialize new collection
localdocs init <name> --crawl <url> [--depth N] [--ai]
localdocs init <name> --local <path> [--ai]

# List collections
localdocs list

# Update existing collection
localdocs update <name>

# Delete collection
localdocs delete <name>

Document Operations

# Search documents
localdocs search <query> [--collection NAME] [--limit N]

# Show specific document
localdocs show <doc-id>

# Get statistics
localdocs stats [--collection NAME]

MCP Server

# Start MCP server (stdio transport)
localdocs serve

# Start with HTTP transport (coming soon)
localdocs serve --port 8080

Configuration

LocalDocs stores configuration in ~/.localdocs-mcp/config.yaml:

storage_path: ~/.localdocs-mcp
default_collection: main

crawl_defaults:
  depth: 2
  word_count_threshold: 50
  excluded_tags: [nav, footer, header]
  cache_enabled: true

processing:
  chunk_size: 2000
  overlap: 200
  generate_examples: true
  
baml:
  model: gpt-4o-mini
  temperature: 0.3

Development

# Install dependencies
uv sync

# Run tests
uv run pytest tests/

# Run specific test file
uv run pytest tests/test_storage.py -v

# Lint and format code
uvx ruff check .
uvx ruff format .

# Type checking
uv run mypy localdocs

Architecture

LocalDocs follows a modular architecture:

CLI Layer: Typer-based command interface
Processing Layer: Web crawling (Crawl4ai) and document processing
Storage Layer: File-based storage with markdown and frontmatter
MCP Layer: FastMCP server implementation
AI Layer: Optional BAML integration for enhanced processing

Storage Format

Documents are stored as markdown files with YAML frontmatter:

---
id: "uuid-here"
collection: "react"
source_url: "https://react.dev/learn/thinking-in-react"
title: "Thinking in React"
chunk: 1
total_chunks: 3
tags: ["react", "component", "state"]
created: 2025-09-04
examples_generated: true
---

# Thinking in React (Part 1/3)

[Document content here]

## Generated Examples

[Example code blocks]