MCP server of web search/fetch functionality using duckduckgo and jina api. no api key required.
MCP server of web search/fetch functionality using duckduckgo and jina api. no api key required. That is what the duckduckgo web search mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
This project provides an MCP (Model Context Protocol) server that allows you to search the web using the DuckDuckGo search engine and optionally fetch and summarize the content of the found URLs.
The server publishes 3 tools. What each one is for:
query — The search query stringlimit — The maximum number of results to return (default: 3, maximum: 10)url — The URL to fetchSetup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Among the search and retrieval options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. DuckDuckGo Web Search's toolset — query, limit, url — is a fair guide to whether it matches your workflow. It is maintained by kouui; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against DuckDuckGo Web Search's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| query | The search query string. |
| limit | The maximum number of results to return (default: 3, maximum: 10). |
| url | The URL to fetch. |
{
"mcpServers": {
"web-search-duckduckgo": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/kouui/web-search-duckduckgo.git@main",
"main.py"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
The simplest web tool that matters — fetch any URL and get model-ready markdown back.
Industrial-strength web extraction — render, scrape, crawl and search entire sites into clean markdown.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Put 6,000+ pre-built scrapers at your assistant's fingertips through one MCP endpoint.