DuckDuckGo MCP Server

by shgsousa

3 stars
242 downloads
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GitHub

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

- 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:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name DuckDuckGo MCP Server
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. 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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