DuckDuckGo MCP Server

The DuckDuckGo MCP server transforms the popular DuckDuckGo search engine into a first‑class AI tool, enabling assistants to retrieve web content directly within their context. By exposing search functions through the Model Context Protocol, developers can embed up‑to‑date information, images, news, and videos into conversational flows without leaving the AI environment. This solves a common pain point: keeping assistants current with real‑time data while maintaining privacy and minimal latency.

At its core, the server offers a suite of search tools—text, image, news, video—and an AI chat endpoint that forwards queries to DuckDuckGo’s own conversational model. Each tool accepts a rich set of parameters such as , , and , allowing fine‑grained control over result locality, content filtering, and recency. The option caps output size to keep responses concise. For image searches, additional filters (, , , , ) let developers target visual assets that fit design or licensing constraints.

The included prompts, notably , let assistants transform raw search outputs into human‑readable summaries. By specifying a (brief or detailed), developers can tailor the verbosity to match the conversational context—quick fact checks, in‑depth research digests, or concise bullet points. This makes the MCP a versatile bridge between structured data and natural language explanations.

Real‑world scenarios range from knowledge‑base augmentation (pulling the latest news about a product) to creative workflows (fetching themed images for design prototypes). In education, tutors can pull up recent research articles or videos to enrich lessons. Content creators might use the image and video tools to gather royalty‑free media for posts, while developers can integrate news feeds into monitoring dashboards. Because the server communicates via MCP, any assistant that supports the protocol—Claude, ChatGPT, or custom agents—can tap into DuckDuckGo’s breadth without writing bespoke API wrappers.

What sets this MCP apart is its tight coupling with DuckDuckGo’s privacy‑first search engine and the breadth of media types it supports. The tool adds a conversational layer, letting assistants ask follow‑up questions directly to DuckDuckGo’s own AI model. Combined with the robust parameter set and summary prompts, developers gain a powerful, low‑overhead solution for injecting fresh, diverse web content into AI workflows while keeping control over filtering and presentation.