A Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.

• 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

To install DuckDuckGo Search Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @nickclyde/duckduckgo-mcp-server --client claude

Install directly from PyPI using uv:

uv pip install duckduckgo-mcp-server

1. Download Claude Desktop
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:

{
    "mcpServers": {
        "ddg-search": {
            "command": "uvx",
            "args": ["duckduckgo-mcp-server"]
        }
    }
}

3. Restart Claude Desktop

For local development, you can use the MCP CLI:

# Run with the MCP Inspector
mcp dev server.py

# Install locally for testing with Claude Desktop
mcp install server.py

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.

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.

• Search: Limited to 30 requests per minute
• Content Fetching: Limited to 20 requests per minute
• Automatic queue management and wait times

• Removes ads and irrelevant content
• Cleans up DuckDuckGo redirect URLs
• Formats results for optimal LLM consumption
• Truncates long content appropriately

• Comprehensive error catching and reporting
• Detailed logging through MCP context
• Graceful degradation on rate limits or timeouts

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

This project is licensed under the MIT License.