What is Ddg Mcp

ddg-mcp is a server that provides DuckDuckGo search capabilities through the Model Context Protocol, enabling users to perform various types of searches including text, images, news, and videos.

Use cases

Use cases for ddg-mcp include searching for academic papers, finding images for design projects, retrieving the latest news articles, and engaging in AI-driven conversations on specific topics.

How to use

To use ddg-mcp, install it via PyPI using ‘uv install ddg-mcp’ or ‘pip install ddg-mcp’. After installation, configure the server and utilize its tools such as ddg-text-search, ddg-image-search, ddg-news-search, and ddg-video-search by providing the required parameters.

Key features

Key features of ddg-mcp include support for multiple search types (text, images, news, videos), customizable search parameters (region, safesearch, timelimit), and integration with various AI chat models for enhanced user interaction.

Where to use

ddg-mcp can be used in various fields including web development, data analysis, content creation, and research, where access to diverse search results is essential.

ddg-mcp MCP server

DuckDuckGo search API MCP - A server that provides DuckDuckGo search capabilities through the Model Context Protocol.

Components

Prompts

The server provides the following prompts:

• search-results-summary: Creates a summary of DuckDuckGo search results
  • Required “query” argument for the search term
  • Optional “style” argument to control detail level (brief/detailed)

Tools

The server implements the following DuckDuckGo search tools:

• ddg-text-search: Search the web for text results using DuckDuckGo

  • Required: “keywords” - Search query keywords
  • Optional: “region”, “safesearch”, “timelimit”, “max_results”

• ddg-image-search: Search the web for images using DuckDuckGo

  • Required: “keywords” - Search query keywords
  • Optional: “region”, “safesearch”, “timelimit”, “size”, “color”, “type_image”, “layout”, “license_image”, “max_results”

• ddg-news-search: Search for news articles using DuckDuckGo

  • Required: “keywords” - Search query keywords
  • Optional: “region”, “safesearch”, “timelimit”, “max_results”

• ddg-video-search: Search for videos using DuckDuckGo

  • Required: “keywords” - Search query keywords
  • Optional: “region”, “safesearch”, “timelimit”, “resolution”, “duration”, “license_videos”, “max_results”

• ddg-ai-chat: Chat with DuckDuckGo AI

  • Required: “keywords” - Message or question to send to the AI
  • Optional: “model” - AI model to use (options: “gpt-4o-mini”, “llama-3.3-70b”, “claude-3-haiku”, “o3-mini”, “mistral-small-3”)

Installation

Prerequisites

• Python 3.9 or higher
• uv (recommended) or pip

Install from PyPI

# Using uv
uv install ddg-mcp

# Using pip
pip install ddg-mcp

Install from Source

1. Clone the repository:

git clone https://github.com/misanthropic-ai/ddg-mcp.git
cd ddg-mcp

2. Install the package:

# Using uv
uv install -e .

# Using pip
pip install -e .

Configuration

Required Dependencies

The server requires the duckduckgo-search package, which will be installed automatically when you install ddg-mcp.

If you need to install it manually:

uv install duckduckgo-search
# or
pip install duckduckgo-search

DuckDuckGo Search Parameters

Common Parameters

These parameters are available for most search types:

• region: Region code for localized results (default: “wt-wt”)

  • Examples: “us-en” (US English), “uk-en” (UK English), “ru-ru” (Russian)
  • See DuckDuckGo regions for more options

• safesearch: Content filtering level (default: “moderate”)

  • “on”: Strict filtering
  • “moderate”: Moderate filtering
  • “off”: No filtering

• timelimit: Time range for results

  • “d”: Last day
  • “w”: Last week
  • “m”: Last month
  • “y”: Last year (not available for news/videos)

• max_results: Maximum number of results to return (default: 10)

Search Operators

You can use these operators in your search keywords:

• cats dogs: Results about cats or dogs
• "cats and dogs": Results for exact term “cats and dogs”
• cats -dogs: Fewer dogs in results
• cats +dogs: More dogs in results
• cats filetype:pdf: PDFs about cats (supported: pdf, doc(x), xls(x), ppt(x), html)
• dogs site:example.com: Pages about dogs from example.com
• cats -site:example.com: Pages about cats, excluding example.com
• intitle:dogs: Page title includes the word “dogs”
• inurl:cats: Page URL includes the word “cats”

Image Search Specific Parameters

• size: “Small”, “Medium”, “Large”, “Wallpaper”
• color: “color”, “Monochrome”, “Red”, “Orange”, “Yellow”, “Green”, “Blue”, “Purple”, “Pink”, “Brown”, “Black”, “Gray”, “Teal”, “White”
• type_image: “photo”, “clipart”, “gif”, “transparent”, “line”
• layout: “Square”, “Tall”, “Wide”
• license_image: “any”, “Public”, “Share”, “ShareCommercially”, “Modify”, “ModifyCommercially”

Video Search Specific Parameters

• resolution: “high”, “standard”
• duration: “short”, “medium”, “long”
• license_videos: “creativeCommon”, “youtube”

AI Chat Models

• gpt-4o-mini: OpenAI’s GPT-4o mini model
• llama-3.3-70b: Meta’s Llama 3.3 70B model
• claude-3-haiku: Anthropic’s Claude 3 Haiku model
• o3-mini: OpenAI’s O3 mini model
• mistral-small-3: Mistral AI’s small model

Quickstart

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development/Unpublished Servers Configuration

``` "mcpServers": { "ddg-mcp": { "command": "uv", "args": [ "--directory", "/Users/shannon/Workspace/artivus/ddg-mcp", "run", "ddg-mcp" ] } } ```

Published Servers Configuration

``` "mcpServers": { "ddg-mcp": { "command": "uvx", "args": [ "ddg-mcp" ] } } ```

Usage Examples

Text Search

Use the ddg-text-search tool to search for "climate change solutions"

Advanced example:

Use the ddg-text-search tool to search for "renewable energy filetype:pdf site:edu" with region "us-en", safesearch "off", timelimit "y", and max_results 20

Image Search

Use the ddg-image-search tool to find images of "renewable energy" with color set to "Green"

Advanced example:

Use the ddg-image-search tool to find images of "mountain landscape" with size "Large", color "Blue", type_image "photo", layout "Wide", and license_image "Public"

News Search

Use the ddg-news-search tool to find recent news about "artificial intelligence" from the last day

Advanced example:

Use the ddg-news-search tool to search for "space exploration" with region "uk-en", timelimit "w", and max_results 15

Video Search

Use the ddg-video-search tool to find videos about "machine learning tutorials" with duration set to "medium"

Advanced example:

Use the ddg-video-search tool to search for "cooking recipes" with resolution "high", duration "short", license_videos "creativeCommon", and max_results 10

AI Chat

Use the ddg-ai-chat tool to ask "What are the latest developments in quantum computing?" using the claude-3-haiku model

Search Results Summary

Use the search-results-summary prompt with query "space exploration" and style "detailed"

Claude config

“ddg-mcp”: {
“command”: “uv”,
“args”: [
“–directory”,
“/PATH/TO/YOUR/INSTALLATION/ddg-mcp”,
“run”,
“ddg-mcp”
]
},

Development

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

uv sync

2. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

uv publish

Note: You’ll need to set PyPI credentials via environment variables or command flags:

• Token: --token or UV_PUBLISH_TOKEN
• Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Automated Publishing with GitHub Actions

This repository includes a GitHub Actions workflow for automated publishing to PyPI. The workflow is triggered when:

1. A new GitHub Release is created
2. The workflow is manually triggered via the GitHub Actions interface

To set up automated publishing:

1. Generate a PyPI API token:

   • Go to https://pypi.org/manage/account/token/
   • Create a new token with scope limited to the ddg-mcp project
   • Copy the token value (you’ll only see it once)

2. Add the token to your GitHub repository secrets:

   • Go to your repository on GitHub
   • Navigate to Settings > Secrets and variables > Actions
   • Click “New repository secret”
   • Name: PYPI_API_TOKEN
   • Value: Paste your PyPI token
   • Click “Add secret”

3. To publish a new version:

   • Update the version number in pyproject.toml
   • Create a new release on GitHub or manually trigger the workflow

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /path/to/your/ddg-mcp run ddg-mcp

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.