DuckDuckResearch MCP Server

The MCP DuckDuckResearch server addresses a common bottleneck in AI‑assisted development: bridging the gap between natural language queries and actionable, structured web data. While many AI assistants can parse text or execute code, they often lack a seamless way to fetch up‑to‑date information from the internet. This MCP server fills that void by combining DuckDuckGo’s privacy‑focused search engine with programmatic web page extraction and screenshot capture, all wrapped in a single, well‑defined protocol.

At its core, the server offers three primary tools. First, search_duckduckgo allows an assistant to query the web and receive a ranked list of results, complete with region‑specific options and safe‑search levels. Second, visit_page takes a URL and returns the page’s content converted into clean Markdown, making it easy to ingest into documentation pipelines or knowledge bases. Third, take_screenshot captures a visual snapshot of the loaded page, automatically optimizing size to balance fidelity and bandwidth. Together these tools enable a workflow where an assistant can ask a question, search for relevant sources, extract the most useful text, and even provide visual context—all without leaving the MCP ecosystem.

Developers benefit from the server’s robust error handling and bot‑detection safeguards. Behind the scenes, Playwright manages a headless Chromium instance that respects rate limits and retries failed requests. The safe‑search configuration ensures that content filtering can be tuned to the needs of different projects, from strict compliance environments to open research settings. By exposing content as Markdown, the server eliminates the need for additional parsing steps, allowing downstream systems to ingest data directly into documentation generators or knowledge graphs.

Real‑world use cases abound. In a technical support scenario, an AI assistant could search for the latest API changes, pull the relevant documentation page, and present a concise summary along with a screenshot of the code snippet. In research workflows, scholars can automatically gather literature reviews by querying academic search engines, extracting abstracts, and compiling them into a single Markdown report. Even marketing teams can use the server to monitor brand mentions, capture competitor landing pages, and generate visual briefs for strategy meetings.

Integration is straightforward: once the MCP server is registered in a client such as Roo Code, its tools appear alongside native extensions. Developers can invoke them using the standard syntax, passing simple JSON arguments. The server handles browser lifecycle management internally, freeing developers from managing state or cleanup logic. This plug‑and‑play model means teams can add web‑scraping capabilities to any AI workflow with minimal friction, unlocking richer, data‑driven interactions without compromising privacy or reliability.