SearXNG MCP Server

The SearXNG MCP Server bridges the privacy‑centric meta search engine SearXNG with AI assistants that understand the Model Context Protocol. By exposing a single tool, it lets Claude or any MCP‑compatible client query dozens of search engines—Google, DuckDuckGo, Bing, and more—without exposing user data to third parties. The server’s design addresses a common pain point for developers: integrating reliable, privacy‑respecting search into conversational agents without having to manage authentication or API keys for each provider.

At its core, the server receives a structured request containing a query string and optional filters such as language, category, time range, and safe‑search level. It forwards the request to a local SearXNG instance, which aggregates results from all configured engines and returns a unified JSON payload. The MCP server then formats this response into the tool’s output schema, allowing the assistant to present a concise list of links, snippets, and metadata. This abstraction eliminates the need for developers to write custom parsers or handle rate limits from individual search services.

Key capabilities include:

• Multi‑category support: Choose from general, news, science, files, images, videos, music, social media, or IT queries.
• Language and time filtering: Narrow results to a specific language code or restrict them to a day, week, month, or year.
• Safe‑search levels: Select from none, moderate, or strict filtering to comply with content policies.
• Fallback resilience: Configure multiple SearXNG instances so the server can route queries if one node fails.

Real‑world scenarios where this MCP shines include: building a privacy‑first research assistant that pulls up scholarly articles; creating a travel chatbot that aggregates flight and hotel listings from multiple providers without tracking users; or developing an educational tool that surfaces up-to‑date news and science content while respecting user language preferences. Because the server is self‑hosted, organizations can keep all search traffic within their own infrastructure, satisfying regulatory requirements and reducing reliance on commercial APIs.

Integration into AI workflows is straightforward. Developers add the server to their MCP configuration, then reference in prompt templates or tool calls. The assistant can automatically decide when to invoke a search, interpret the results, and continue the conversation—all while preserving user privacy. The SearXNG MCP Server thus offers a powerful, configurable bridge between open‑source meta search and modern AI assistants.