MCP Server
About
MCP Server is an implementation of the Model Context Protocol (MCP) that provides tools for AI agents and LLMs — including web search via SearXNG and secure file system operations. It is intended for developers building AI applications that need sandboxed tool access.
Details
- License
- Apache-2.0 license
Explore
- Web Search: Perform web searches with customizable parameters using SearXNG
- File System Operations: Read, write, and manage files with security controls
- Extensible Architecture: Add custom tools following the MCP protocol
- Docker Support: Easy deployment with Docker and docker-compose
- Command-line Interface: Simple management through CLI
- Resources and Prompts: Full MCP specification support for resources and prompts
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Requirements
- Python 3.13 (required)
- MCP SDK ≥ 1.6.0
- UV package manager for dependency management
- Docker (optional, for containerized deployment)
@mcp.tool()
def search(query: str, num_results: int = 5) -> dict:
"""Search the web"""
return {"result": f"Processed {param1} with {param2}"}
```
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp server": {
"mcp-server-avinash539": {
"command": "docker",
"args": [
"build",
"-t",
"mcp-server",
"."
]
}
}
}
}McpServers
{
"mcp-server-avinash539": {
"command": "docker",
"args": [
"build",
"-t",
"mcp-server",
"."
]
}
}A Model Context Protocol (MCP) server implementation that provides various tools for AI agents and LLMs, including search capabilities powered by SearXNG and file system operations.
Features
- Web Search: Perform web searches with customizable parameters using SearXNG
- File System Operations: Read, write, and manage files with security controls
- Extensible Architecture: Add custom tools following the MCP protocol
- Docker Support: Easy deployment with Docker and docker-compose
- Command-line Interface: Simple management through CLI
- Resources and Prompts: Full MCP specification support for resources and prompts
Requirements
- Python 3.13 (required)
- MCP SDK ≥ 1.6.0
- UV package manager for dependency management
- Docker (optional, for containerized deployment)
Implementation Approaches
This project demonstrates two approaches to implementing MCP servers:
1. FastMCP Decorator Approach (Recommended)
The simplified approach using FastMCP decorators in mcp_server/server.py:
```python
from mcp.server.fastmcp import FastMCP
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