What are the runtime requirements?
Python 3.13 is required, along with MCP SDK ≥ 1.6.0 and the uv package manager. Docker is optional but recommended.
@avinash539
基本信息
This is a repository for mcp servers
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-server-avinash539": {
"command": "docker",
"args": [
"build",
"-t",
"mcp-server",
"."
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
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.
You can deploy MCP Server with Docker (recommended) or install directly using Python 3.13 and the uv package manager. The server is started via the command python -m mcp_server start and can be configured through environment variables or integrated with the Claude Desktop client using a claude_desktop_config.json entry.
Python 3.13 is required, along with MCP SDK ≥ 1.6.0 and the uv package manager. Docker is optional but recommended.
You can either build and run the Docker image (`docker build -t mcp-server && docker-compose up`) or install directly with uv and run `python -m mcp_server start`.
No. Web search requires a running SearXNG instance. You must provide its URL via the `MCP_SEARXNG__INSTANCE_URL` environment variable.
The server is configured through environment variables covering host/port (e.g., `MCP_SERVER__HOST`, `MCP_SERVER__PORT`), SearXNG settings, log level, and a data directory.
You can extend the server using the FastMCP decorator approach (`@mcp.tool()`) or by following the original architecture documented in the developer guide.
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