DuckDuckGo Search▌
by nickclyde
DuckDuckGo Search integrates web search and content parsing, ensuring privacy-focused results for large language model u
Integrates with DuckDuckGo to provide web search capabilities, content fetching, and parsing, with results formatted for large language model consumption.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / LLM applications needing web search
- / Research and information gathering
- / Content discovery workflows
capabilities
- / Search web content using DuckDuckGo
- / Filter results by date and region
- / Retrieve news articles and video content
- / Configure safe search levels
- / Paginate through search results
- / Cache search results
what it does
Provides web search functionality through DuckDuckGo with support for different result types, filtering, and pagination. Note: This repository is deprecated in favor of mcp-omnisearch.
about
DuckDuckGo Search is a community-built MCP server published by nickclyde that provides AI assistants with tools and capabilities via the Model Context Protocol. DuckDuckGo Search integrates web search and content parsing, ensuring privacy-focused results for large language model u It is categorized under search web. This server exposes 2 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install DuckDuckGo Search in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport. This server supports remote connections over HTTP, so no local installation is required.
license
MIT
DuckDuckGo Search is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
DuckDuckGo Search MCP Server
A Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.
Quick Start
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:
uv pip install duckduckgo-mcp-server
Usage
Running with Claude Desktop
- Download Claude Desktop
- 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
- On macOS:
Add the following configuration:
Basic Configuration (No SafeSearch, No Default Region):
{
"mcpServers": {
"ddg-search": {
"command": "uvx",
"args": ["duckduckgo-mcp-server"]
}
}
}
With SafeSearch and Region Configuration:
{
"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
- Restart Claude Desktop
Running with Claude Code
- Download Claude Code
- Ensure
uvenvis installed and theuvxcommand is available - 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:
# 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.
Development
For local development:
# 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
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 stringmax_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
async def fetch_content(url: str) -> str
Fetches and parses content from a webpage.
Parameters:
url: The webpage URL to fetch content from
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_SEARCHenvironment variable- Controlled by administrators, not modifiable by AI assistants
- Filters inappropriate content based on the selected level
- Uses DuckDuckGo's official
kpparameter
-
Region Localization:
- Default region set via
DDG_REGIONenvironment variable - Can be overridden per search request by AI assistants
- Improves result relevance for specific geographic regions
- Default region set via
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.
FAQ
- What is the DuckDuckGo Search MCP server?
- DuckDuckGo Search is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for DuckDuckGo Search?
- This profile displays 37 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Web Research & Information Gathering
Fetch and extract information from websites automatically
Example
Research competitor pricing, scrape product reviews, monitor news mentions
Automate 5-10 hours/week of manual web research
Content Monitoring & Alerts
Track website changes, new content, price updates
Example
Monitor competitor blog for new posts, track stock availability, watch for pricing changes
Stay informed without manual checking, never miss important updates
Data Extraction & Aggregation
Extract structured data from multiple websites
Example
Compile product listings from 10 e-commerce sites, aggregate job postings, collect real estate data
Build datasets 100x faster than manual copying
API-less Integration
Interact with services that don't offer APIs
Example
Check form submissions, validate website functionality, test user flows
Automate interactions with any website, even without API
Implementation Guide▌
Prerequisites
- ›Claude Desktop or Cursor with MCP support
- ›Understanding of web scraping ethics and robots.txt
- ›Rate limiting awareness to avoid overwhelming target sites
- ›Knowledge of legal restrictions on data collection
Time Estimate
20-40 minutes including configuration and testing
Installation Steps
- 1.Install web automation MCP server via npm or pip
- 2.Configure allowed domains and rate limits in MCP config
- 3.Test with simple fetch: 'Get content from example.com'
- 4.Progress to extraction: 'Extract all product prices from this page'
- 5.Set up monitoring: 'Check this URL daily for changes'
- 6.Parse structured data: 'Create CSV from this table'
- 7.Respect robots.txt and rate limits always
Troubleshooting
- ⚠403 Forbidden: Website blocks bots—respect their wishes, use official API instead
- ⚠Rate limit errors: Slow down requests, add delays between fetches
- ⚠Stale data: Target site changed HTML structure—update selectors
- ⚠Timeout errors: Site is slow or blocking—increase timeout, try different user agent
- ⚠JavaScript-rendered content: Use headless browser MCP servers for dynamic sites
Best Practices▌
✓ Do
- +Check robots.txt and respect crawl rules
- +Rate limit requests: 1-2 requests/second maximum
- +Use official APIs when available instead of scraping
- +Identify your bot with descriptive user agent
- +Cache results to minimize repeated requests
- +Handle errors gracefully with retries and fallbacks
- +Validate extracted data for accuracy
✗ Don't
- −Don't scrape sites that explicitly forbid it (robots.txt, ToS)
- −Don't overwhelm servers with rapid requests—use rate limiting
- −Don't scrape personal data without consent and legal basis
- −Don't ignore copyright on extracted content
- −Don't assume HTML structure is stable—handle changes
- −Don't use scraped data for commercial purposes without permission
💡 Pro Tips
- ★Use CSS selectors or XPath for robust data extraction
- ★Set up monitoring alerts for extraction failures (structure changed)
- ★Implement exponential backoff for retries on failures
- ★Store raw HTML for reprocessing if extraction logic changes
- ★Combine with data analysis tools for insights from extracted data
- ★Consider using official APIs or RSS feeds as more stable alternatives
Technical Details▌
Architecture
MCP server handles HTTP requests, HTML parsing, JavaScript rendering (if headless browser), and returns structured data to Claude.
Protocols
- HTTP/HTTPS
- WebSocket (for real-time sites)
- Puppeteer/Playwright (for JavaScript sites)
Compatibility
- Static HTML sites
- JavaScript-rendered SPAs (with headless browser)
- REST APIs
- GraphQL endpoints
When to Use This▌
✓ Use When
Use for research automation, content monitoring, data aggregation from multiple sources, and when official APIs don't exist. Best for read-only information gathering.
✗ Avoid When
Avoid for sites with APIs (use API instead), sites that explicitly forbid scraping, when data is copyrighted, or for login-required content without proper authorization.
Integration▌
- →Scheduled monitoring with change detection
- →Multi-source data aggregation pipelines
- →Fallback to web scraping when API rate limits hit
- →Headless browser for JavaScript-heavy sites
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★37 reviews- ★★★★★Chaitanya Patil· Dec 16, 2024
DuckDuckGo Search reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Sofia Yang· Dec 12, 2024
Useful MCP listing: DuckDuckGo Search is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Piyush G· Nov 7, 2024
I recommend DuckDuckGo Search for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Sofia Menon· Nov 3, 2024
Strong directory entry: DuckDuckGo Search surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Shikha Mishra· Oct 26, 2024
Strong directory entry: DuckDuckGo Search surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Sofia Abbas· Oct 22, 2024
I recommend DuckDuckGo Search for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Sofia Sanchez· Sep 21, 2024
DuckDuckGo Search is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Sofia Thompson· Sep 13, 2024
DuckDuckGo Search has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Mei Sharma· Sep 5, 2024
We evaluated DuckDuckGo Search against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Mei Thompson· Sep 1, 2024
Strong directory entry: DuckDuckGo Search surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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