🔍 SearXNG MCP Server

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

✨ Features

• 🔒 Privacy-first - No tracking, no data collection via SearXNG

• 🌐 Multi-engine - Aggregates results from Google, Bing, DuckDuckGo, Brave, and more

• 🎯 Specialized search - Web, images, videos, and news search

• ⚡ Fast - Optimized with minimal tool set (4 tools)

• 🐳 Docker included - SearXNG instance setup included

• 🛠️ Easy setup - Python-based with UV package manager

Related MCP server: MCP Deep Search

📦 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

⚙️ 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
  }
}

🔧 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 ⭐

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
✓ Major development 1 (HIGH CONFIDENCE - confirmed by 12 sources)
✓ Emerging trend 2 (MEDIUM - reported by 5 sources)
⚠ 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)
..."

💡 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")]

🐳 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

🛠️ Development

Run tests:

pytest

Format code:

black .

Type checking:

mypy .

Lint:

ruff .

🎯 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

📁 Project Structure

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

📄 License

MIT License

🙏 Credits

• SearXNG - Privacy-respecting metasearch engine

• Model Context Protocol - MCP specification

• Built with FastMCP