What are the prerequisites for running the server?
Python 3.9+ and access to a running SearXNG instance (local or remote) are required. Docker and uv are optional for containerized or faster setup.
@The-AI-Workshops
基本情報
An MCP sse implementation of the Model Context Protocol (MCP) server integrated with SearXNG for providing AI agents with powerful, privacy-respecting search capabilities.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"searxng-mcp-server": {
"command": "docker",
"args": [
"run",
"-d",
"--name=searxng",
"-p",
"32768:8080",
"-v",
"/root/searxng:/etc/searxng",
"\\"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
The SearXNG MCP Server is an MCP SSE implementation that integrates the SearXNG metasearch engine with the Model Context Protocol. It enables AI agents to perform privacy-respecting web searches through a SearXNG instance. The server serves both as a ready-to-use tool and as a template for building custom MCP servers.
Install the server using uv, Docker, or Smithery. Ensure Python 3.9+ and a running SearXNG instance are available. Configure a .env file with SEARXNG_BASE_URL, HOST, PORT, and TRANSPORT (sse or stdio). Run server.py directly, via Docker, or through Smithery depending on your chosen transport. Connect your MCP client (e.g., Claude Desktop, Windsurf) using the appropriate SSE URL or stdio configuration.
Python 3.9+ and access to a running SearXNG instance (local or remote) are required. Docker and uv are optional for containerized or faster setup.
The server supports both SSE (Server-Sent Events) and stdio transports. Set the `TRANSPORT` environment variable to `sse` or `stdio` in your `.env` file.
Set the `SEARXNG_BASE_URL` environment variable in your `.env` file, for example `http://172.17.0.1:32768` (default Docker bridge address).
Yes, n8n users should use `host.docker.internal` instead of `localhost` in the SSE URL because n8n runs inside a separate container.
Yes, the repository is designed as a template. Add custom tools by decorating methods with `@mcp.tool()`, and add resources or prompts with `@mcp.resource()` and `@mcp.prompt()`.

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