duckduckgo-mcp-server

# DuckDuckGo Search MCP Server [](https://smithery.ai/server/@nickclyde/duckduckgo-mcp-server) A Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing. <a href="https://glama.ai/mcp/servers/phcus2gcpn"> <img width="380" height="200" src="https://glama.ai/mcp/servers/phcus2gcpn/badge" alt="DuckDuckGo Server MCP server" /> </a> ## Features - **Web Search**: Search DuckDuckGo with advanced rate limiting and result formatting - **Content Fetching**: Retrieve and parse webpage content with intelligent text extraction - **Rate Limiting**: Built-in protection against rate limits for both search and content fetching - **Error Handling**: Comprehensive error handling and logging - **LLM-Friendly Output**: Results formatted specifically for large language model consumption ## Installation ### Installing via Smithery To install DuckDuckGo Search Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@nickclyde/duckduckgo-mcp-server): ```bash npx -y @smithery/cli install @nickclyde/duckduckgo-mcp-server --client claude ``` ### Installing via `uv` Install directly from PyPI using `uv`: ```bash uv pip install duckduckgo-mcp-server ``` ## Usage ### Running with Claude Desktop 1. Download [Claude Desktop](https://claude.ai/download) 2. Create or edit your Claude Desktop configuration: - On macOS: `~/Library/Application Support/Claude/claude_desktop_config.json` - On Windows: `%APPDATA%\Claude\claude_desktop_config.json` Add the following configuration: ```json { "mcpServers": { "ddg-search": { "command": "uvx", "args": ["duckduckgo-mcp-server"] } } } ``` 3. Restart Claude Desktop ### Development For local development, you can use the MCP CLI: ```bash # Run with the MCP Inspector mcp dev server.py # Install locally for testing with Claude Desktop mcp install server.py ``` ## Available Tools ### 1. Search Tool ```python async def search(query: str, max_results: int = 10) -> str ``` Performs a web search on DuckDuckGo and returns formatted results. **Parameters:** - `query`: Search query string - `max_results`: Maximum number of results to return (default: 10) **Returns:** Formatted string containing search results with titles, URLs, and snippets. ### 2. Content Fetching Tool ```python async def fetch_content(url: str) -> str ``` Fetches and parses content from a webpage. **Parameters:** - `url`: The webpage URL to fetch content from **Returns:** Cleaned and formatted text content from the webpage. ## Features in Detail ### Rate Limiting - Search: Limited to 30 requests per minute - Content Fetching: Limited to 20 requests per minute - Automatic queue management and wait times ### Result Processing - Removes ads and irrelevant content - Cleans up DuckDuckGo redirect URLs - Formats results for optimal LLM consumption - Truncates long content appropriately ### Error Handling - Comprehensive error catching and reporting - Detailed logging through MCP context - Graceful degradation on rate limits or timeouts ## Contributing Issues and pull requests are welcome! Some areas for potential improvement: - Additional search parameters (region, language, etc.) - Enhanced content parsing options - Caching layer for frequently accessed content - Additional rate limiting strategies ## License This project is licensed under the MIT License.