An MCP sse implementation of the Model Context Protocol (MCP) server integrated with SearXNG for providing AI agents with powerful, privacy-respecting search capabilities.
{
"mcpServers": {
"searxng-mcp-server": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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An MCP sse implementation of the Model Context Protocol (MCP) server integrated with SearXNG for providing AI agents with powerful, privacy-respecting search capabilities.
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License not specified.
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Last commit 325 days ago. 19 stars.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
No known vulnerabilities.
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An MCP sse implementation of the Model Context Protocol (MCP) server integrated with SearXNG for providing AI agents with powerful, privacy-respecting search capabilities.
This project demonstrates how to build an MCP server that enables AI agents to perform web searches using a SearXNG instance. It serves as a practical template for creating your own MCP servers, using SearXNG as a backend.
The implementation follows the best practices laid out by Anthropic for building MCP servers, allowing seamless integration with any MCP-compatible client.
You must have a SearXNG server running and accessible. The recommended way is via Docker:
docker run -d --name=searxng -p 32768:8080 -v "/root/searxng:/etc/searxng" \
-e "BASE_URL=http://0.0.0.0:32768/" \
-e "INSTANCE_NAME=home" \
--restart always searxng/searxng
/root/searxng.http://172.17.0.1:32768 by default (see .env).Install uv if you don't have it:
pip install uv
Clone this repository:
git clone https://github.com/The-AI-Workshops/searxng-mcp-server.git
cd searxng-mcp-server/dev/searXNG-mcp
Install dependencies:
uv pip install -r requirements.txt
Create a .env file based on the provided example:
nano .env
# Edit .env as needed
Configure your environment variables in the .env file (see Configuration section).
Build the Docker image:
docker build -t mcp/searxng-mcp .
Create a .env file and configure your environment variables.
Run the Docker image:
docker run -d --env-file ./.env -p 32769:32769 mcp/searxng-mcp
Smithery is a command-line tool for managing AI agent tools and MCP servers.
Install Smithery if you don't have it (see Smithery documentation for various installation methods, e.g., using pipx):
pipx install smithery
Install the SearXNG MCP server using Smithery:
smithery install @The-AI-Workshops/searxng-mcp-server
This will install the server and its dependencies into a dedicated environment managed by Smithery.
After installation, Smithery will provide you with the path to the installed server. You will need to navigate to this directory to configure it. For example, if Smithery installs tools into ~/.smithery/tools/, the path might be ~/.smithery/tools/The-AI-Workshops/searxng-mcp-server.
Create a .env file in the server's directory by copying the example:
# Example:
# cd ~/.smithery/tools/The-AI-Workshops/searxng-mcp-server
cp .env.example .env
nano .env
# Edit .env as needed
Configure your environment variables in the .env file (see Configuration section).
The following environment variables can be configured in your .env file:
| Variable | Description | Example | |--------------------|---------------------------------------------|-----------------------------------------| | SEARXNG_BASE_URL | Base URL of your SearXNG instance | http://172.17.0.1:32768 | | HOST | Host to bind to when using SSE transport | 0.0.0.0 | | PORT | Port to listen on when using SSE transport | 32769 | | TRAN