mcp-duckduckgo-search

⚠️ Notice

This repository is no longer maintained.

The functionality of this tool is now available in mcp-omnisearch, which combines multiple MCP tools in one unified package.

Please use mcp-omnisearch instead.

A Model Context Protocol (MCP) server for integrating DuckDuckGo search capabilities with LLMs. This server provides comprehensive web search functionality with support for various result types and filtering options.

Features

• 🔍 Comprehensive web search using DuckDuckGo's search engine

• 📊 Rich result types including:

  • Knowledge graph information

  • Organic search results

  • News articles

  • Video content

  • Image results

  • Related searches

• 🌍 Region-specific search support

• 🛡️ Configurable safe search levels

• 📅 Date-based filtering options

• 📄 Pagination support

• 💾 Built-in result caching

• 🔒 Safe search options (off, moderate, strict)

Configuration

This server requires configuration through your MCP client. Here are examples for different environments:

Cline Configuration

Add this to your Cline MCP settings:

Claude Desktop with WSL Configuration

For WSL environments, add this to your Claude Desktop configuration:

Environment Variables

The server requires the following environment variable:

• SERPAPI_KEY: Your SerpAPI key (required)

API

The server implements a single MCP tool with configurable parameters:

ddg_search

Perform web searches using the DuckDuckGo search engine.

Parameters:

• query (string, required): Search query

• region (string, optional): Region code (e.g., us-en, uk-en) (default: us-en)

• safe_search (string, optional): Safe search level (off, moderate, strict) (default: moderate)

• date_filter (string, optional): Filter results by date:

  • 'd': past day

  • 'w': past week

  • 'm': past month

  • 'y': past year

  • Custom range: '2023-01-01..2023-12-31'

• start (number, optional): Result offset for pagination

• no_cache (boolean, optional): Bypass cache for fresh results (default: false)

Response includes:

• Knowledge graph data when available

• Organic search results

• News articles

• Video content

• Image results

• Related searches

• Search metadata

Development

Setup

1. Clone the repository

2. Install dependencies:

3. Build the project:

4. Run in development mode:

Publishing

The project uses changesets for version management. To publish:

1. Create a changeset:

2. Version the package:

3. Publish to npm:

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see the LICENSE file for details.

Acknowledgments

• Built on the Model Context Protocol

• Powered by DuckDuckGo through SerpAPI