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This repository is no longer maintained.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.

Related MCP server: MCP JinaAI Search Server

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