SearxngMCP

netixc/SearxngMCP

3.3

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SearXNG MCP Server is a privacy-focused Model Context Protocol server that enhances Claude's web search capabilities using the SearXNG metasearch engine.

Tools
3
Resources
0
Prompts
0

šŸ” SearXNG MCP Server

A privacy-focused Model Context Protocol (MCP) server that provides Claude with web search capabilities through SearXNG metasearch engine.

āœØ Features

  • šŸ”’ Privacy-first - No tracking, no data collection via SearXNG
  • šŸŒ Multi-engine - Aggregates results from Google, Bing, DuckDuckGo, Brave, and more
  • šŸŽÆ Specialized search - Web, images, videos, and news search
  • āš” Fast - Optimized with minimal tool set (4 tools)
  • šŸ³ Docker included - SearXNG instance setup included
  • šŸ› ļø Easy setup - Python-based with UV package manager

šŸ“¦ Installation

Prerequisites

  • Python 3.10 or higher
  • Docker and Docker Compose
  • Git

Quick Install

1. Clone repository:

git clone https://github.com/netixc/SearxngMCP.git
cd SearxngMCP

2. Configure SearXNG:

Edit the following files with your settings:

  • docker-compose.yml - Replace YOUR_IP with your server's IP address
  • docker-compose.yml - Replace CHANGE_THIS_SECRET_KEY with a secret key
  • searxng/settings.yml - Replace CHANGE_THIS_TO_YOUR_OWN_SECRET_KEY with the same secret key
  • searxng-config/config.json - Replace YOUR_IP with your server's IP address

Generate a secret key:

openssl rand -hex 32

3. Start SearXNG instance:

docker compose up -d

SearXNG will be available at http://YOUR_IP:8080

4. Install MCP server (using UV - recommended):

# Create venv and install
uv venv
source .venv/bin/activate  # Linux/macOS
uv pip install -e ".[dev]"

5. Verify installation:

# Check SearXNG is running
curl http://YOUR_IP:8080

āš™ļø Configuration

MCP Client Setup

Add to your MCP settings (e.g., Claude Desktop config):

{
  "mcpServers": {
    "searxng": {
      "command": "/absolute/path/to/SearxngMCP/run-server.sh"
    }
  }
}

SearXNG Configuration

The SearXNG instance is configured via searxng/settings.yml:

  • Default engines: Google, Bing, DuckDuckGo, Brave, Wikipedia, YouTube
  • JSON API enabled for MCP access
  • Privacy features enabled (no tracking)
  • Accessible on your LAN at YOUR_IP:8080

IMPORTANT: Before starting Docker, replace the following in your config files:

  1. docker-compose.yml: Replace YOUR_IP and CHANGE_THIS_SECRET_KEY
  2. searxng/settings.yml: Replace CHANGE_THIS_TO_YOUR_OWN_SECRET_KEY
  3. searxng-config/config.json: Replace YOUR_IP

Generate secret key: openssl rand -hex 32

MCP Server Configuration

Edit searxng-config/config.json (replace YOUR_IP with your server's IP):

{
  "searxng": {
    "url": "http://YOUR_IP:8080",
    "timeout": 10
  },
  "logging": {
    "level": "INFO",
    "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    "file": null
  }
}

šŸ”§ Available Tools

The server provides 3 optimized tools designed for efficient research:

1. search - Quick Web/News Search

Quick single search for web or news content.

Use when:

  • Need quick information or simple lookup
  • User asks for a basic web search
  • Looking for news on a topic

Parameters:

  • query* - What to search for
  • category - "general" (default) or "news"
  • engines - Optional: Specific engines (e.g., "google,bing")
  • max_results - Number of results (default: 10, max: 50)

Example:

User: What's the latest Python release?
Claude: [Calls search("latest Python release", category="general")]

2. search_media - Images & Videos

Search for images or videos.

Use when:

  • User wants to find images or photos
  • Looking for video content
  • "show me pictures of..." or "find videos about..."

Parameters:

  • query* - What to find
  • media_type - "images" (default) or "videos"
  • engines - Optional: Specific engines
  • max_results - Number of results (default: 10, max: 50)

Example:

User: Show me pictures of Northern Lights
Claude: [Calls search_media("Northern Lights", media_type="images")]

3. research_topic - Deep Research ā­

Multi-search research with automatic analysis and synthesis.

Use when:

  • User wants comprehensive research or briefing
  • Need to validate information across multiple sources
  • User asks to "research", "investigate", or "analyze"
  • Creating detailed reports with cross-referenced sources

What it does:

  • Runs 2-6 searches automatically using different strategies
  • Searches multiple engines (Google, Bing, DuckDuckGo, Brave, Wikipedia)
  • Combines general web + news sources
  • Deduplicates results across all searches
  • Returns 15-50 UNIQUE sources
  • Instructs Claude to analyze and synthesize (not just list sources)

Critical behavior: After gathering sources, Claude is instructed to:

  1. Read and analyze ALL sources
  2. Cross-reference claims across sources
  3. Identify high-confidence facts (confirmed by many sources)
  4. Note contradictions or single-source claims
  5. Create comprehensive briefing with executive summary
  6. Assess source quality and credibility

Parameters:

  • query* - Research topic or question
  • depth - Research thoroughness:
    • "quick" - 2 searches, ~15 unique sources
    • "standard" - 4 searches, ~30 unique sources (recommended)
    • "deep" - 6 searches, ~50 unique sources

Example:

User: Research the latest AI developments and give me a briefing
Claude: [Calls research_topic("latest AI developments 2025", depth="standard")]

Claude receives 32 unique sources, then synthesizes:

"# AI Developments Briefing (2025)

## Executive Summary
Based on analysis of 32 sources from Google, Bing, DuckDuckGo, and Wikipedia...

## Key Findings
āœ“ Major development 1 (HIGH CONFIDENCE - confirmed by 12 sources)
āœ“ Emerging trend 2 (MEDIUM - reported by 5 sources)
āš  Claim 3 (LOW - single source, needs verification)

## Contradictions
Source A says X, but Sources B, C, D report Y...

## Source Quality
Most reliable: Google News (8 sources), Wikipedia (3 sources)
..."

šŸ’” Usage Examples

General search:

User: What is the latest news about AI?
Claude: [Calls search("latest AI news")]

Image search:

User: Show me pictures of Northern Lights
Claude: [Calls search_images("Northern Lights")]

Video search:

User: Find Python tutorial videos
Claude: [Calls search_videos("Python tutorial")]

News search:

User: What's happening with climate change?
Claude: [Calls search_news("climate change")]

šŸ³ Docker Management

Start SearXNG:

docker-compose up -d

Stop SearXNG:

docker-compose down

View logs:

docker-compose logs -f searxng

Rebuild:

docker-compose down
docker-compose up -d --build

šŸ› ļø Development

Run tests:

pytest

Format code:

black .

Type checking:

mypy .

Lint:

ruff .

šŸŽÆ Why Only 4 Tools?

This MCP server is optimized for efficiency:

  1. Focused functionality - Each tool has a clear, distinct purpose
  2. LLM-friendly - Tool descriptions include "Use this when..." guidance
  3. Low context - Minimal tool set reduces token usage
  4. Privacy-first - SearXNG aggregates without tracking

Unlike direct search engine APIs, SearXNG provides:

  • Privacy protection (no tracking)
  • Multi-engine aggregation
  • Self-hosted control
  • No API keys needed

šŸ“ Project Structure

SearxngMCP/
ā”œā”€ā”€ docker-compose.yml          # SearXNG Docker setup
ā”œā”€ā”€ searxng/
ā”‚   ā””ā”€ā”€ settings.yml            # SearXNG configuration
ā”œā”€ā”€ src/searxng_mcp/
ā”‚   ā”œā”€ā”€ server.py               # Main MCP server
ā”‚   ā”œā”€ā”€ config/                 # Configuration handling
ā”‚   ā”‚   ā”œā”€ā”€ models.py
ā”‚   ā”‚   ā””ā”€ā”€ loader.py
ā”‚   ā””ā”€ā”€ tools/                  # Search tool implementations
ā”‚       ā””ā”€ā”€ search.py
ā”œā”€ā”€ searxng-config/
ā”‚   ā””ā”€ā”€ config.json             # MCP configuration
ā”œā”€ā”€ run-server.sh               # Server startup script
ā”œā”€ā”€ pyproject.toml              # Dependencies
ā””ā”€ā”€ README.md

šŸ“„ License

MIT License

šŸ™ Credits