A Model Context Protocol (MCP) server that enables AI assistants to perform web searches using SearXNG, a privacy-respecting metasearch engine.
What is tisDDM searxng mcp
SearXNG MCP Server
A Model Context Protocol (MCP) server that enables AI assistants to perform web searches using SearXNG, a privacy-respecting metasearch engine. Works out-of-the-box with zero additional deployment by automatically selecting a random instance from SearX.space, while also supporting private instances with basic authentication.
Features
- Zero-configuration setup: Works immediately by using a random public instance from SearX.space
- Private instance support: Connect to your own SearXNG instance with optional basic authentication
- Perform web searches with customizable parameters
- Support for multiple search engines
- Privacy-focused search results
- Markdown-formatted search results
- Sensible default values for all parameters
CAVEAT - Public Instances might be unavailabe for this purpose and return "Request failed with status code 429"
Installation
Prerequisites
- Node.js (v16 or higher)
- npm (v7 or higher)
- Access to a SearXNG instance (self-hosted or public)
Install from source
# Clone the repository
git clone https://github.com/tisDDM/searxng-mcp.git
cd searxng-mcp
# Install dependencies
npm install
# Build the project
npm run build
Configuration
The SearXNG MCP server can be configured with the following environment variables:
SEARXNG_URL
(optional): The URL of your SearXNG instance (e.g.,https://searx.example.com
). If not provided, a random public instance from SearX.space will be automatically selected, making the server usable with zero additional deployment.USE_RANDOM_INSTANCE
(optional): Set to "false" to disable random instance selection when no URL is provided. Default is "true".SEARXNG_USERNAME
(optional): Username for basic authentication when connecting to a private instanceSEARXNG_PASSWORD
(optional): Password for basic authentication when connecting to a private instance
You can set these environment variables in a .env
file in the root directory of the project:
SEARXNG_URL=https://searx.example.com
SEARXNG_USERNAME=your_username
SEARXNG_PASSWORD=your_password
Usage
Running the server
# If installed globally
searxngmcp
# If installed from source
node build/index.js
Integrating with Claude Desktop
- Open Claude Desktop
- Go to Settings > MCP Servers
- Add a new MCP server with the following configuration:
{ "mcpServers": { "searxngmcp": { "command": "searxngmcp", "env": { // Optional: If not provided, a random public instance will be used "SEARXNG_URL": "https://searx.example.com", // Optional: Only needed for private instances with authentication "SEARXNG_USERNAME": "your_username", "SEARXNG_PASSWORD": "your_password" }, "disabled": false, "autoApprove": [] } } }
Integrating with Claude in VSCode
- Open VSCode
- Go to Settings > Extensions > Claude > MCP Settings
- Add a new MCP server with the following configuration:
{ "mcpServers": { "searxngmcp": { "command": "node", "args": ["/path/to/searxng-mcp/build/index.js"], "env": { // Optional: If not provided, a random public instance will be used "SEARXNG_URL": "https://searx.example.com", // Optional: Only needed for private instances with authentication "SEARXNG_USERNAME": "your_username", "SEARXNG_PASSWORD": "your_password" }, "disabled": false, "autoApprove": [] } } }
Usage with Smolagents
SearXNG MCP can be easily integrated with Smolagents, a lightweight framework for building AI agents. This allows you to create powerful research agents that can search the web and process the results:
from smolagents import CodeAgent, LiteLLMModel, ToolCollection
from mcp import StdioServerParameters
# Configure the SearXNG MCP server
server_parameters = StdioServerParameters(
command="node",
args=["path/to/searxng-mcp/build/index.js"],
env={
"SEARXNG_URL": "https://your-searxng-instance.com",
"SEARXNG_USERNAME": "your_username", # Optional
"SEARXNG_PASSWORD": "your_password" # Optional
}
)
# Create a tool collection from the MCP server
with ToolCollection.from_mcp(server_parameters) as tool_collection:
# Initialize your LLM model
model = LiteLLMModel(
model_id="your-model-id",
api_key="your-api-key",
temperature=0.7
)
# Create an agent with the search tools
search_agent = CodeAgent(
name="search_agent",
tools=tool_collection.tools,
model=model
)
# Run the agent with a search prompt
result = search_agent.run(
"Perform a search about: 'climate change solutions' and summarize the top 5 results."
)
print(result)
Available Tools
searxngsearch
Perform web searches using SearXNG, a privacy-respecting metasearch engine. Returns relevant web content with customizable parameters.
Parameters
Parameter | Type | Description | Default | Required |
---|---|---|---|---|
query | string | Search query | - | Yes |
language | string | Language code for search results (e.g., 'en', 'de', 'fr') | 'en' | No |
time_range | string | Time range for search results. Options: 'day', 'week', 'month', 'year' | null | No |
categories | array of strings | Categories to search in (e.g., 'general', 'images', 'news') | null | No |
engines | array of strings | Specific search engines to use | null | No |
safesearch | number | Safe search level: 0 (off), 1 (moderate), 2 (strict) | 1 | No |
pageno | number | Page number for results. Must be minimum 1 | 1 | No |
max_results | number | Maximum number of search results to return. Range: 1-50 | 10 | No |
Example
// Example request
const result = await client.callTool('searxngsearch', {
query: 'climate change solutions',
language: 'en',
time_range: 'year',
categories: ['general', 'news'],
safesearch: 1,
max_results: 5
});
Development
Setup
# Clone the repository
git clone https://github.com/tisDDM/searxng-mcp.git
cd searxng-mcp
# Install dependencies
npm install
Build
npm run build
Watch mode (for development)
npm run watch
Testing with MCP Inspector
npm run inspector
License
MIT
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Frequently Asked Questions
What is MCP?
MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.
What are MCP Servers?
MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.
How do MCP Servers work?
MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.
Are MCP Servers secure?
Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.
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