Documentation Crawler & MCP Server

This project provides a toolset to crawl websites, generate Markdown documentation, and make that documentation searchable via a Model Context Protocol (MCP) server, designed for integration with tools like Cursor.

Features

• Web Crawler (crawler_cli):
  • Crawls websites starting from a given URL using crawl4ai.
  • Configurable crawl depth, URL patterns (include/exclude), content types, etc.
  • Optional cleaning of HTML before Markdown conversion (removes nav links, headers, footers).
  • Generates a single, consolidated Markdown file from crawled content.
  • Saves output to ./storage/ by default.
• MCP Server (mcp_server):
  • Loads Markdown files from the ./storage/ directory.
  • Parses Markdown into semantic chunks based on headings.
  • Generates vector embeddings for each chunk using sentence-transformers (multi-qa-mpnet-base-dot-v1).
  • Caching: Utilizes a cache file (storage/document_chunks_cache.pkl) to store processed chunks and embeddings.
    • First Run: The initial server startup after crawling new documents may take some time as it needs to parse, chunk, and generate embeddings for all content.
    • Subsequent Runs: If the cache file exists and the modification times of the source .md files in ./storage/ haven’t changed, the server loads directly from the cache, resulting in much faster startup times.
    • Cache Invalidation: The cache is automatically invalidated and regenerated if any .md file in ./storage/ is modified, added, or removed since the cache was last created.
  • Exposes MCP tools via fastmcp for clients like Cursor:
    • list_documents: Lists available crawled documents.
    • get_document_headings: Retrieves the heading structure for a document.
    • search_documentation: Performs semantic search over document chunks using vector similarity.
• Cursor Integration: Designed to run the MCP server via stdio transport for use within Cursor.

Workflow

1. Crawl: Use the crawler_cli tool to crawl a website and generate a .md file in ./storage/.
2. Run Server: Configure and run the mcp_server (typically managed by an MCP client like Cursor).
3. Load & Embed: The server automatically loads, chunks, and embeds the content from the .md files in ./storage/.
4. Query: Use the MCP client (e.g., Cursor Agent) to interact with the server’s tools (list_documents, search_documentation, etc.) to query the crawled content.

Setup

This project uses uv for dependency management and execution.

1. Install uv: Follow the instructions on the uv website.

2. Clone the repository:

   git clone https://github.com/alizdavoodi/MCPDocSearch.git
   cd MCPDocSearch
   

3. Install dependencies:

   uv sync
   

   This command creates a virtual environment (usually .venv) and installs all dependencies listed in pyproject.toml.

Usage

1. Crawling Documentation

Run the crawler using the crawl.py script or directly via uv run.

Basic Example:

uv run python crawl.py https://docs.example.com

This will crawl https://docs.example.com with default settings and save the output to ./storage/docs.example.com.md.

Example with Options:

uv run python crawl.py https://docs.another.site --output ./storage/custom_name.md --max-depth 2 --keyword "API" --keyword "Reference" --exclude-pattern "*blog*"

View all options:

uv run python crawl.py --help

Key options include:

• --output/-o: Specify output file path.
• --max-depth/-d: Set crawl depth (must be between 1 and 5).
• --include-pattern/--exclude-pattern: Filter URLs to crawl.
• --keyword/-k: Keywords for relevance scoring during crawl.
• --remove-links/--keep-links: Control HTML cleaning.
• --cache-mode: Control crawl4ai caching (DEFAULT, BYPASS, FORCE_REFRESH).
• --wait-for: Wait for a specific time (seconds) or CSS selector before capturing content (e.g., 5 or 'css:.content'). Useful for pages with delayed loading.
• --js-code: Execute custom JavaScript on the page before capturing content.
• --page-load-timeout: Set the maximum time (seconds) to wait for a page to load.
• --wait-for-js-render/--no-wait-for-js-render: Enable a specific script to better handle JavaScript-heavy Single Page Applications (SPAs) by scrolling and clicking potential “load more” buttons. Automatically sets a default wait time if --wait-for is not specified.

Refining Crawls with Patterns and Depth

Sometimes, you might want to crawl only a specific subsection of a documentation site. This often requires some trial and error with --include-pattern and --max-depth.

• --include-pattern: Restricts the crawler to only follow links whose URLs match the given pattern(s). Use wildcards (*) for flexibility.
• --max-depth: Controls how many “clicks” away from the starting URL the crawler will go. A depth of 1 means it only crawls pages directly linked from the start URL. A depth of 2 means it crawls those pages and pages linked from them (if they also match include patterns), and so on.

Example: Crawling only the Pulsar Admin API section

Suppose you want only the content under https://pulsar.apache.org/docs/4.0.x/admin-api-*.

1. Start URL: You could start at the overview page: https://pulsar.apache.org/docs/4.0.x/admin-api-overview/.
2. Include Pattern: You only want links containing admin-api: --include-pattern "*admin-api*".
3. Max Depth: You need to figure out how many levels deep the admin API links go from the starting page. Start with 2 and increase if needed.
4. Verbose Mode: Use -v to see which URLs are being visited or skipped, which helps debug the patterns and depth.

uv run python crawl.py https://pulsar.apache.org/docs/4.0.x/admin-api-overview/ -v --include-pattern "*admin-api*" --max-depth 2

Check the output file (./storage/pulsar.apache.org.md by default in this case). If pages are missing, try increasing --max-depth to 3. If too many unrelated pages are included, make the --include-pattern more specific or add --exclude-pattern rules.

2. Running the MCP Server

The MCP server is designed to be run by an MCP client like Cursor via the stdio transport. The command to run the server is:

python -m mcp_server.main

However, it needs to be run from the project’s root directory (MCPDocSearch) so that Python can find the mcp_server module.

Caution: Embedding Time

The MCP server generates embeddings locally the first time it runs or whenever the source Markdown files in ./storage/ change. This process involves loading a machine learning model and processing all the text chunks.

• Time Varies: The time required for embedding generation can vary significantly based on:
  • Hardware: Systems with a compatible GPU (CUDA or Apple Silicon/MPS) will be much faster than CPU-only systems.
  • Data Size: The total number of Markdown files and their content length directly impacts processing time.
• Be Patient: For large documentation sets or on slower hardware, the initial startup (or startup after changes) might take several minutes. Subsequent startups using the cache will be much faster.

3. Configuring Cursor/Claude for Desktop

To use this server with Cursor, create a .cursor/mcp.json file in the root of this project (MCPDocSearch/.cursor/mcp.json) with the following content:

{
  "mcpServers": {
    "doc-query-server": {
      "command": "uv",
      "args": [
        "--directory",
        // IMPORTANT: Replace with the ABSOLUTE path to this project directory on your machine
        "/path/to/your/MCPDocSearch",
        "run",
        "python",
        "-m",
        "mcp_server.main"
      ],
      "env": {}
    }
  }
}

Explanation:

• "doc-query-server": A name for the server within Cursor.
• "command": "uv": Specifies uv as the command runner.
• "args":
  • "--directory", "/path/to/your/MCPDocSearch": Crucially, tells uv to change its working directory to your project root before running the command. Replace /path/to/your/MCPDocSearch with the actual absolute path on your system.
  • "run", "python", "-m", "mcp_server.main": The command uv will execute within the correct directory and virtual environment.

After saving this file and restarting Cursor, the “doc-query-server” should become available in Cursor’s MCP settings and usable by the Agent (e.g., @doc-query-server search documentation for "how to install").

For Claude for Desktop, you can use this official documentation to set up the MCP server

Dependencies

Key libraries used:

• crawl4ai: Core web crawling functionality.
• fastmcp: MCP server implementation.
• sentence-transformers: Generating text embeddings.
• torch: Required by sentence-transformers.
• typer: Building the crawler CLI.
• uv: Project and environment management.
• beautifulsoup4 (via crawl4ai): HTML parsing.
• rich: Enhanced terminal output.

Architecture

The project follows this basic flow:

1. crawler_cli: You run this tool, providing a starting URL and options.
2. Crawling (crawl4ai): The tool uses crawl4ai to fetch web pages, following links based on configured rules (depth, patterns).
3. Cleaning (crawler_cli/markdown.py): Optionally, HTML content is cleaned (removing navigation, links) using BeautifulSoup.
4. Markdown Generation (crawl4ai): Cleaned HTML is converted to Markdown.
5. Storage (./storage/): The generated Markdown content is saved to a file in the ./storage/ directory.
6. mcp_server Startup: When the MCP server starts (usually via Cursor’s config), it runs mcp_server/data_loader.py.
7. Loading & Caching: The data loader checks for a cache file (.pkl). If valid, it loads chunks and embeddings from the cache. Otherwise, it reads .md files from ./storage/.
8. Chunking & Embedding: Markdown files are parsed into chunks based on headings. Embeddings are generated for each chunk using sentence-transformers and stored in memory (and saved to cache).
9. MCP Tools (mcp_server/mcp_tools.py): The server exposes tools (list_documents, search_documentation, etc.) via fastmcp.
10. Querying (Cursor): An MCP client like Cursor can call these tools. search_documentation uses the pre-computed embeddings to find relevant chunks based on semantic similarity to the query.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request.

Security Notes

• Pickle Cache: This project uses Python’s pickle module to cache processed data (storage/document_chunks_cache.pkl). Unpickling data from untrusted sources can be insecure. Ensure that the ./storage/ directory is only writable by trusted users/processes.