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SearXNG MCP Server

by netixc
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Python
Hybrid

🔍 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

📦 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

One-click Deploy
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

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