MCPDocSearch

# 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`](https://github.com/astral-sh/uv) for dependency management and execution. 1. **Install `uv`**: Follow the instructions on the [uv website](https://github.com/astral-sh/uv). 2. **Clone the repository:** ```bash git clone https://github.com/alizdavoodi/MCPDocSearch.git cd MCPDocSearch ``` 3. **Install dependencies:** ```bash 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:** ```bash 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:** ```bash 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:** ```bash 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. ```bash 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: ```bash 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: ```json { "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](https://modelcontextprotocol.io/quickstart/server#mac-os-linux) 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](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.