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# 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.
