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    Duckduckgo Mcp Server

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

    555 stars
    Python
    Updated Oct 19, 2025

    Table of Contents

    • Features
    • Installation
    • Installing via Smithery
    • Installing via uv
    • Usage
    • Running with Claude Desktop
    • Development
    • Available Tools
    • 1. Search Tool
    • 2. Content Fetching Tool
    • Features in Detail
    • Rate Limiting
    • Result Processing
    • Error Handling
    • Contributing
    • License

    Table of Contents

    • Features
    • Installation
    • Installing via Smithery
    • Installing via uv
    • Usage
    • Running with Claude Desktop
    • Development
    • Available Tools
    • 1. Search Tool
    • 2. Content Fetching Tool
    • Features in Detail
    • Rate Limiting
    • Result Processing
    • Error Handling
    • Contributing
    • License

    Documentation

    DuckDuckGo Search MCP Server

    smithery badge

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

    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:

    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

    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.

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