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Mcp Server SearXNG n8n

MCP Server

Integrate SearXNG search into n8n workflows

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Updated Jul 2, 2025

About

A Model Context Protocol server that connects SearXNG search capabilities to n8n, allowing automated searches with customizable parameters directly from workflow nodes.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

Overview

The MCP Server for SearXNG n8n Integration bridges the powerful, privacy‑focused search engine SearXNG with n8n’s visual workflow automation. By exposing a lightweight MCP server that offers a single tool, developers can embed web‑search capabilities directly into AI‑driven workflows without writing custom connectors or handling API keys. This eliminates the need for third‑party search APIs, preserving user privacy while still delivering real‑time information to conversational agents.

The server’s value lies in its seamless integration with the MCP ecosystem. Once installed, an n8n workflow can add a single MCP node that connects to the server via . The node’s operation section allows developers to choose the tool and supply search parameters as a JSON payload. Because the payload can incorporate n8n expressions, queries can be dynamically generated from chat history or other workflow data. The server translates these parameters into a SearXNG request, retrieves results in the requested format (JSON, CSV, RSS, or HTML), and returns them to the AI assistant for further processing.

Key capabilities include:

  • Customizable search scope: Specify categories, engines, language, and time range to tailor results.
  • Privacy controls: Use levels and image proxying to enforce content filtering.
  • Plugin management: Enable or disable SearXNG plugins and search engines on the fly.
  • Output flexibility: Return structured JSON for programmatic use or HTML for direct display in chat interfaces.

Typical use cases involve building knowledge‑base bots that answer user queries with up‑to‑date web content, automating data collection for research pipelines, or creating internal search assistants that respect corporate privacy policies. In an AI workflow, the MCP node can be chained after a language model’s prompt generation step, feeding live search results back into the assistant for contextual grounding or fact‑checking.

What sets this server apart is its minimal footprint and zero‑cost architecture. It leverages the existing SearXNG instance, avoiding external API limits or costs, while n8n’s visual editor makes it accessible to developers who prefer low‑code solutions. The combination of privacy, flexibility, and ease of integration makes it a standout choice for any project that requires reliable web search within an AI‑powered automation stack.