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DuckDuckGo Search

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nickclyde

A Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.

Publishernickclyde
Repositoryduckduckgo-mcp-server
LanguagePython
Forks
167
Stars
1.1K
Available tools
2
Transport typestdio, streamable-http
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    DuckDuckGo Search exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 2 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    1.1K stars and 167 forks from the linked repository.

DuckDuckGo Search MCP Server

PyPI version PyPI downloads Python versions

A Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.

Quick Start

bash
uvx duckduckgo-mcp-server

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

Install from PyPI using uv:

bash
uv pip install duckduckgo-mcp-server

Usage

Running with Claude Desktop

  1. Download Claude Desktop
  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:

Basic Configuration (No SafeSearch, No Default Region):

json
{
    "mcpServers": {
        "ddg-search": {
            "command": "uvx",
            "args": ["duckduckgo-mcp-server"]
        }
    }
}

With SafeSearch and Region Configuration:

json
{
    "mcpServers": {
        "ddg-search": {
            "command": "uvx",
            "args": ["duckduckgo-mcp-server"],
            "env": {
                "DDG_SAFE_SEARCH": "STRICT",
                "DDG_REGION": "cn-zh"
            }
        }
    }
}

Configuration Options:

  • DDG_SAFE_SEARCH: SafeSearch filtering level (optional)
    • STRICT: Maximum content filtering (kp=1)
    • MODERATE: Balanced filtering (kp=-1, default if not specified)
    • OFF: No content filtering (kp=-2)
  • DDG_REGION: Default region/language code (optional, examples below)
    • us-en: United States (English)
    • cn-zh: China (Chinese)
    • jp-ja: Japan (Japanese)
    • wt-wt: No specific region
    • Leave empty for DuckDuckGo's default behavior
  1. Restart Claude Desktop

Running with Claude Code

  1. Download Claude Code
  2. Ensure uvenv is installed and the uvx command is available
  3. Add the MCP server: claude mcp add ddg-search uvx duckduckgo-mcp-server

Running with SSE or Streamable HTTP

The server supports alternative transports for use with other MCP clients:

bash
# SSE transport
uvx duckduckgo-mcp-server --transport sse

# Streamable HTTP transport
uvx duckduckgo-mcp-server --transport streamable-http

The default transport is stdio, which is used by Claude Desktop and Claude Code.

When running with sse or streamable-http, override the default bind address (127.0.0.1:8000) with the --host and --port flags:

bash
uvx duckduckgo-mcp-server --transport streamable-http --host 0.0.0.0 --port 7070

Fetch Backend (bypassing bot detection)

Some sites block the default httpx client because of its distinctive TLS fingerprint, regardless of User-Agent — Cloudflare Bot Management and similar filters key on the JA3/TLS handshake, not on headers. An opt-in backend, curl (implemented via curl_cffi), impersonates a real Chrome browser's TLS handshake and passes through those checks.

Installation:

bash
# Default install (httpx only)
uv pip install duckduckgo-mcp-server

# With the optional browser backend
uv pip install "duckduckgo-mcp-server[browser]"

Backend options:

ValueBehaviorNeeds [browser]
httpxLightweight async HTTP. Default. Works on most sites.no
curlUses curl_cffi with Chrome 131 TLS impersonation. Passes TLS-fingerprint-based filters.yes
autoTries httpx first; on 403 or a Cloudflare challenge response, retries with curl.yes

Two ways to configure the backend:

  1. Server-wide default via the --fetch-backend CLI flag (applies to every fetch_content call):

    bash
    # Default behavior — uses httpx
    uvx duckduckgo-mcp-server
    
    # Force curl for every fetch (requires the [browser] extra)
    uvx --with "duckduckgo-mcp-server[browser]" duckduckgo-mcp-server --fetch-backend curl
    
    # Try httpx first, fall back to curl on 403 / Cloudflare challenge
    uvx --with "duckduckgo-mcp-server[browser]" duckduckgo-mcp-server --fetch-backend auto
  2. Per-call override via the backend argument on the fetch_content tool (overrides the CLI default for that single call). The tool exposes backend in its input schema, so an MCP client can choose "httpx", "curl", or "auto" on a fetch-by-fetch basis.

The search tool always uses httpx — DuckDuckGo's search endpoint doesn't require impersonation.

The default stays httpx so users who don't need the impersonation don't pay for the extra dependency.

Development

For local development:

bash
# Install dependencies
uv sync

# Run with the MCP Inspector
mcp dev src/duckduckgo_mcp_server/server.py

# Install locally for testing with Claude Desktop
mcp install src/duckduckgo_mcp_server/server.py

# Run all tests
uv run python -m pytest src/duckduckgo_mcp_server/ -v

# Run only unit tests
uv run python -m pytest src/duckduckgo_mcp_server/test_server.py -v

# Run only e2e tests
uv run python -m pytest src/duckduckgo_mcp_server/test_e2e.py -v

Available Tools

1. Search Tool

python
async def search(query: str, max_results: int = 10, region: str = "") -> 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)
  • region: (Optional) Region/language code to override the default. Leave empty to use the configured default region.

Region Code Examples:

  • us-en: United States (English)
  • cn-zh: China (Chinese)
  • jp-ja: Japan (Japanese)
  • de-de: Germany (German)
  • fr-fr: France (French)
  • wt-wt: No specific region

Returns: Formatted string containing search results with titles, URLs, and snippets.

Example Usage:

  • Search with default settings: search("python tutorial")
  • Search with specific region: search("latest news", region="jp-ja") for Japanese news

2. Content Fetching Tool

python
async def fetch_content(
    url: str,
    start_index: int = 0,
    max_length: int = 8000,
    backend: Optional[str] = None,
) -> str

Fetches and parses content from a webpage.

Parameters:

  • url: The webpage URL to fetch content from
  • start_index: Character offset to start reading from (for pagination)
  • max_length: Maximum number of characters to return
  • backend: Optional per-call override of the default fetch backend ("httpx", "curl", or "auto"). When omitted, uses whatever was set via --fetch-backend at server startup.

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

Content Safety

  • SafeSearch Filtering: Configured at server startup via DDG_SAFE_SEARCH environment variable

    • Controlled by administrators, not modifiable by AI assistants
    • Filters inappropriate content based on the selected level
    • Uses DuckDuckGo's official kp parameter
  • Region Localization:

    • Default region set via DDG_REGION environment variable
    • Can be overridden per search request by AI assistants
    • Improves result relevance for specific geographic regions

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:

  • Enhanced content parsing options
  • Caching layer for frequently accessed content
  • Additional rate limiting strategies

License

This project is licensed under the MIT License.

Star History

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Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "ddg-search": {
      "command": "uvx",
      "args": [
        "duckduckgo-mcp-server"
      ]
    }
  }
}

Available Tools

  • search
    Search DuckDuckGo and return formatted results.
    
    Args:
        query: The search query string
        max_results: Maximum number of results to return (default: 10)
        ctx: MCP context for logging
    
  • fetch_content
    Fetch and parse content from a webpage URL.
    
    Args:
        url: The webpage URL to fetch content from
        ctx: MCP context for logging
    

Use DuckDuckGo Search MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once DuckDuckGo Search is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. You can run MCP locally on your device or connect to a remote MCP server URL.

Option 1: Use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add DuckDuckGo Search as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the DuckDuckGo Search MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "duckduckgo-search": {
      "command": "npx",
      "args": [
        "-y",
        "duckduckgo-mcp-server"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the DuckDuckGo Search MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the DuckDuckGo Search MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use DuckDuckGo Search to help me with this task?
DuckDuckGo Search
Sure. I read it.
Here is what I found using DuckDuckGo Search.

Option 2: Add an MCP server URL

Use this when DuckDuckGo Search is already hosted remotely or your team wants one shared connector that multiple users can access.

1

Open MCP connectors

In TypingMind, go to Plugins, open MCP connectors, then choose Add URL.

  1. Open TypingMind in your browser.
  2. Go to Plugins.
  3. Open MCP connectors.
  4. Click Add URL.
TypingMind Add Custom MCP Server URL form
2

Paste the server URL

Enter your server URL in the Server URL field. Add a connection name, description, icon, custom HTTP headers, or OAuth client settings if the server requires them.

  1. Paste your server URL into the Server URL field.
  2. Enter a connection name for DuckDuckGo Search.
  3. Add a description and icon if you want it to be easier to identify.
  4. Add custom HTTP headers or OAuth client details if the server requires authentication.
3

Create the connection

Click Create connection, then return to the Plugins list and confirm the new MCP connection is active.

  1. Click Create connection.
  2. Return to the MCP connectors list.
  3. Confirm the DuckDuckGo Search connection appears as active.
  4. Refresh the plugin list if the connection does not appear immediately.
4

Switch models without reconnecting

Start a chat with your preferred model, enable the DuckDuckGo Search tools from Plugins, and switch to another model whenever needed. The MCP connection stays available to the TypingMind workspace.

  1. Start a new chat in TypingMind.
  2. Select the AI model you want to use.
  3. Enable the DuckDuckGo Search tools from Plugins.
  4. Ask the model to use the tool when needed.
  5. Switch to another AI model and reuse the same MCP connection.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use DuckDuckGo Search to help me with this task?
DuckDuckGo Search
Sure. I read it.
Here is what I found using DuckDuckGo Search.

Frequently asked questions

What is the DuckDuckGo Search MCP server used for?

DuckDuckGo Search is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use DuckDuckGo Search MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use DuckDuckGo Search with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use DuckDuckGo Search MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With DuckDuckGo Search connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect DuckDuckGo Search MCP to TypingMind?

DuckDuckGo Search can be connected in TypingMind with the local MCP connector or by adding a remote MCP server URL. Use the local connector when the server needs access to files, apps, or private resources on your device, and use a server URL when the MCP server is hosted remotely.

What tools does DuckDuckGo Search MCP provide in TypingMind?

DuckDuckGo Search exposes 2 MCP tools that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use DuckDuckGo Search MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If DuckDuckGo Search requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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