MCP DuckDuckGo Search Plugin
Provides web search functionality with detailed results, related searches, pagination support, domain extraction, advanced filtering by site and time period, enhanced content extraction, basic web spidering, metadata extraction, and content structure analysis.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP DuckDuckGo Search Plugin find recent articles about AI advancements in healthcare"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP DuckDuckGo
A Model Context Protocol (MCP) server that provides web search capabilities using DuckDuckGo. This server enables LLMs to search the web and retrieve detailed content from websites through structured data extraction.
Key Features
Fast and reliable. Uses DuckDuckGo's web interface with robust HTML parsing
LLM-friendly. Returns structured data optimized for AI consumption
Content extraction. Intelligently extracts and summarizes webpage content
Related searches. Generates contextual search suggestions
Related MCP server: MCP DuckDuckGo
Requirements
Python 3.10 or newer
VS Code, Cursor, Windsurf, Claude Desktop, Goose or any other MCP client
Getting started
First, install the DuckDuckGo MCP server with your client.
Standard config works in most of the tools:
{
"mcpServers": {
"duckduckgo-search": {
"command": "mcp-duckduckgo"
}
}
}Claude Code
Use the Claude Code CLI to add the DuckDuckGo MCP server:
claude mcp add duckduckgo-search mcp-duckduckgoFor global configuration (available in all projects):
claude mcp add duckduckgo-search --scope user mcp-duckduckgoClaude Desktop
Follow the MCP install guide, use the standard config above.
Cursor
Go to Cursor Settings -> MCP .
Click the button to install:
Or install manually:
Go to Cursor Settings -> MCP -> Add new MCP Server. Name to your liking, use command type with the command mcp-duckduckgo.
VS Code
Click the button to install:
Or install manually:
Follow the MCP install guide, use the standard config above.
You can also install the DuckDuckGo MCP server using the VS Code CLI:
code --add-mcp '{"name":"duckduckgo-search","command":"mcp-duckduckgo"}'After installation, the DuckDuckGo MCP server will be available for use with your GitHub Copilot agent in VS Code.
Windsurf
Follow Windsurf MCP documentation. Use the standard config above.
Goose
Click the button to install:
Or install manually:
Go to Advanced settings -> Extensions -> Add custom extension. Name to your liking, use type STDIO, and set the command to mcp-duckduckgo. Click "Add Extension".
LM Studio
Click the button to install:
Or install manually:
Go to Program in the right sidebar -> Install -> Edit mcp.json. Use the standard config above.
Configuration
DuckDuckGo MCP server supports following arguments:
mcp-duckduckgo --helpAvailable options:
--port PORT Port number for the MCP server (default: 3000)
--version Show program's version number and exit
--help Show help message and exitEnvironment Variables
MCP_PORT: Set the port number for the server (default: 3000)
Example usage:
# Set port via environment variable
export MCP_PORT=8080
mcp-duckduckgo
# Or set it inline
MCP_PORT=8080 mcp-duckduckgoAvailable Tools
web_search
Title: Web Search
Description: Search the web using DuckDuckGo
Parameters:
query(string): Search query (max 400 characters)max_results(number, optional): Maximum number of results to return (1-20, default 10)
Read-only: false
get_page_content
Title: Get Page Content
Description: Retrieve and extract content from a web page
Parameters:
url(string): URL to fetch content from
Read-only: false
suggest_related_searches
Title: Suggest Related Searches
Description: Generate contextual search suggestions based on a query
Parameters:
query(string): Original search querymax_suggestions(number, optional): Maximum suggestions to return (1-10, default 5)
Read-only: true
Installation from Source
If you need to install from source or development:
Using uv (Recommended)
uv is a fast Python package manager:
# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install from GitHub
uv tool install git+https://github.com/gianlucamazza/mcp-duckduckgo.gitUsing pip
# Clone and install
git clone https://github.com/gianlucamazza/mcp-duckduckgo.git
cd mcp-duckduckgo
pip install -e .Development Installation
git clone https://github.com/gianlucamazza/mcp-duckduckgo.git
cd mcp-duckduckgo
# Install in development mode
pip install -e .
# Run tests
pip install -e ".[test]"
pytestLicense
Repository
Available Tools
3 toolsget_page_contentC
Fetch and extract content from a web page.
Returns the page title, description, and main content.
Inputs: url (required). Returns: result
web_searchB
Search the web using DuckDuckGo.
Returns a list of search results with titles, URLs, descriptions, and domains.
Inputs: query (required), max_results. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: get_page_content extracts content from a specific URL, web_search performs general web searches, and suggest_related_searches provides autocomplete suggestions. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb_noun pattern (get_page_content, suggest_related_searches, web_search) using snake_case. The naming is predictable and readable, with no deviations in style or convention.
With 3 tools, the server is well-scoped for a DuckDuckGo search plugin. Each tool serves a distinct and essential function in the search workflow, from performing searches to extracting content and getting suggestions, with no unnecessary redundancy.
The tool surface covers core search operations effectively, including searching, content extraction, and related suggestions. A minor gap exists in advanced search features like filtering by date or region, but agents can work around this with the provided tools for most use cases.
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