MCP Fetch

# MCP Fetch A Model Context Protocol server that provides web content fetching capabilities **with robots.txt checking removed**. This server enables LLMs to retrieve and process content from web pages, converting HTML to markdown for easier consumption. This is a modified version of the original mcp-server-fetch that removes all robots.txt checking, allowing unrestricted access to web content. > [!CAUTION] > This server can access local/internal IP addresses and may represent a security risk. Exercise caution when using this MCP server to ensure this does not expose any sensitive data. Additionally, this version ignores robots.txt restrictions which may violate some websites' access policies. The fetch tool will truncate the response, but by using the `start_index` argument, you can specify where to start the content extraction. This lets models read a webpage in chunks, until they find the information they need. ## Available Tools - `fetch` - Fetches a URL from the internet and extracts its contents as markdown. - `url` (string, required): URL to fetch - `max_length` (integer, optional): Maximum number of characters to return (default: 5000) - `start_index` (integer, optional): Start content from this character index (default: 0) - `raw` (boolean, optional): Get raw content without markdown conversion (default: false) ## Available Prompts - **fetch** - Fetch a URL and extract its contents as markdown - Arguments: - `url` (string, required): URL to fetch ## Installation and Usage ### Local Development Setup 1. **Clone or download the source code:** ```bash git clone https://github.com/LangGPT/mcp-fetch.git cd mcp-fetch ``` 2. **Install dependencies using uv:** ```bash uv sync ``` 3. **Test the server:** ```bash uv run python -m mcp_fetch --help ``` ### Using with Claude Desktop (Local Source) 1. **Create Claude Desktop configuration:** ```json { "mcpServers": { "mcp-fetch": { "command": "uv", "args": [ "run", "--directory", "/path/to/your/mcp-fetch", "python", "-m", "mcp_fetch" ] } } } ``` 2. **Add configuration to Claude Desktop:** - macOS: `~/Library/Application Support/Claude/claude_desktop_config.json` - Windows: `%APPDATA%/Claude/claude_desktop_config.json` - Linux: `~/.config/Claude/claude_desktop_config.json` 3. **Restart Claude Desktop** to load the new server. ### Using with VS Code (Local Source) Add to your VS Code settings or `.vscode/mcp.json`: ```json { "mcp": { "servers": { "mcp-fetch": { "command": "uv", "args": [ "run", "--directory", "/path/to/your/mcp-fetch", "python", "-m", "mcp_fetch" ] } } } } ``` ### Installation via Package Manager #### Using uv (recommended) When using [`uv`](https://docs.astral.sh/uv/) no specific installation is needed. We will use [`uvx`](https://docs.astral.sh/uv/guides/tools/) to directly run *mcp-fetch*: ```bash uvx mcp-fetch ``` #### Using pip ```bash pip install mcp-fetch ``` After installation, run it as: ```bash python -m mcp_fetch ``` ### Package Manager Configuration #### Claude Desktop with uvx ```json { "mcpServers": { "mcp-fetch": { "command": "uvx", "args": ["mcp-fetch"] } } } ``` #### VS Code with uvx ```json { "mcp": { "servers": { "mcp-fetch": { "command": "uvx", "args": ["mcp-fetch"] } } } } ``` ## Development ### Setting up Development Environment 1. **Install development dependencies:** ```bash uv sync --dev ``` 2. **Run linting and type checking:** ```bash uv run ruff check uv run pyright ``` 3. **Build the package:** ```bash uv build ``` ### Testing Test the server locally: ```bash uv run python -m mcp_fetch ``` Use the MCP inspector for debugging: ```bash npx @modelcontextprotocol/inspector uv run python -m mcp_fetch ``` ### Making Changes 1. Edit the source code in `src/mcp_fetch/` 2. Test your changes with `uv run python -m mcp_fetch` 3. Update version in `pyproject.toml` if needed 4. Run tests and linting ## Publishing ### Publishing to PyPI 1. **Build the package:** ```bash uv build ``` 2. **Publish to PyPI:** ```bash uv publish ``` Or using twine: ```bash pip install twine twine upload dist/* ``` ### Publishing to GitHub 1. **Initialize git repository (if not already done):** ```bash git init git branch -m main ``` 2. **Add and commit files:** ```bash git add . git commit -m "Initial commit: MCP Web Fetch server without robots.txt checking" ``` 3. **Create GitHub repository and push:** ```bash # Create repository on GitHub first, then: git remote add origin https://github.com/LangGPT/mcp-fetch.git git push -u origin main ``` 4. **Create a release on GitHub:** - Go to your repository on GitHub - Click "Releases" → "Create a new release" - Tag version: `v0.6.3` - Release title: `v0.6.3 - MCP Fetch` - Describe your changes - Publish release ### Building Docker Image ```bash docker build -t mcp-fetch . docker tag mcp-fetch LangGPT/mcp-fetch:latest docker push LangGPT/mcp-fetch:latest ``` ## Customization ### robots.txt **This version has robots.txt checking completely removed.** All web requests will proceed regardless of robots.txt restrictions. ### User-agent By default, depending on if the request came from the model (via a tool), or was user initiated (via a prompt), the server will use either the user-agent: ``` ModelContextProtocol/1.0 (Autonomous; +https://github.com/modelcontextprotocol/servers) ``` or: ``` ModelContextProtocol/1.0 (User-Specified; +https://github.com/modelcontextprotocol/servers) ``` This can be customized by adding the argument `--user-agent=YourUserAgent` to the `args` list in the configuration. ### Proxy The server can be configured to use a proxy by using the `--proxy-url` argument. ## Debugging You can use the MCP inspector to debug the server: For local development: ```bash npx @modelcontextprotocol/inspector uv run python -m mcp_fetch ``` For uvx installations: ```bash npx @modelcontextprotocol/inspector uvx mcp-fetch ``` ## Contributing We encourage contributions to help expand and improve mcp-fetch. Whether you want to add new tools, enhance existing functionality, or improve documentation, your input is valuable. ## License mcp-fetch is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.