DuckDuckGo MCP Server
About
A web search tool and API powered by DuckDuckGo, Gradio, and MCP, providing both a user-friendly web interface and Claude Desktop tool integration. It fetches web search results, extracts summaries, and retrieves the full content of web pages in markdown format.
Details
- Author
- shgsousa
- GitHub stars
- 3
- Downloads
- 242
- Categories
- Search
Jump to
- Web‑based search interface using DuckDuckGo
- Real‑time search results with full content
- Markdown‑formatted output
- Configurable number of results
- AI‑powered content summarization (requires API key)
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
DuckDuckGo MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Run the Docker container (docker run -p 7860:7860 ddg-mcp-server) and access the application at http://localhost:7860. Optionally configure an OpenAI-compatible API key via environment variables (OPENAI_API_URL, ACCESS_TOKEN) to enable AI‑powered content summarization.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"duckduckgo mcp server": {
"ddg_mcp_server": {
"command": "docker",
"args": [
"build",
"-t",
"ddg-mcp-server",
"."
]
}
}
}
}
McpServers
{
"ddg_mcp_server": {
"command": "docker",
"args": [
"build",
"-t",
"ddg-mcp-server",
"."
]
}
}
DuckDuckGo MCP Server
A web-based search interface using DuckDuckGo's search API, built with Python and Gradio.
Docker Setup
Prerequisites
- Docker installed on your system
- Git (optional, for cloning the repository)
Building the Docker Image
1. Clone the repository (if you haven't already):
git clone <repository-url>
cd ddg_mcp_server
2. Build the Docker image:
docker build -t ddg-mcp-server .
Running the Container
Run the container with port 7860 mapped to your host:
docker run -p 7860:7860 ddg-mcp-server
The application will be available at:
- http://localhost:7860
- http://127.0.0.1:7860
Troubleshooting
If you cannot connect to the application:
1. Verify the container is running:
docker ps
2. Check the container logs:
docker logs $(docker ps -q)
3. Try stopping any existing containers and starting fresh:
docker stop $(docker ps -q)
docker run -p 7860:7860 ddg-mcp-server
Features
- Web-based search interface using DuckDuckGo
- Real-time search results with full content
- Markdown-formatted output
- Configurable number of results
- AI-powered content summarization (see SUMMARIZATION.md for details)
Development
The application is built with:
- Python 3.10
- Gradio for the web interface
- DuckDuckGo Search API
- BeautifulSoup4 for web scraping
- Markdownify for content conversion
API Configuration for Summarization
This application supports content summarization using OpenAI's API or any compatible API service. To enable this feature:
1. Copy the .env.example file to .env:
cp .env.example .env
2. Edit the .env file and set your API credentials:
OPENAI_API_URL=https://api.openai.com/v1
ACCESS_TOKEN=your_api_key_here
Notes:
- OPENAI_API_URL defaults to the official OpenAI API server if not specified
- ACCESS_TOKEN is required for the summarization feature to work
- You can use any OpenAI-compatible API by changing the OPENAI_API_URL
Running with Docker and API Credentials
To run the Docker container with your API credentials:
docker run -p 7860:7860 \
-e OPENAI_API_URL="https://api.openai.com/v1" \
-e ACCESS_TOKEN="your_api_key_here" \
ddg-mcp-server
Testing the API Connection
After configuring your API credentials, you can test if the connection works correctly:
python main.py --test-api
This will validate your API credentials without starting the full server.
Model Configuration
The AI model used for summarization can be configured in the config.py file:
```python
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