@matdev83
基本信息
Allow any MCP-capable LLM agent to communicate with or delegate tasks to any other LLM available through the OpenRouter.ai API.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"llm-wrapper-mcp-server": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}
}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。
LLM Wrapper MCP Server is a Model Context Protocol (MCP) STDIO server that allows any MCP-capable LLM agent to communicate with or delegate tasks to any other LLM available through the OpenRouter.ai API. It’s built for developers who need a standardized interface to integrate multiple LLM backends into their applications.
Install the package via pip install llm-wrapper-mcp-server, create a .env file with your OPENROUTER_API_KEY, then run the server with python -m llm_wrapper_mcp_server [OPTIONS] (e.g., --model your-org/your-model). The server communicates over stdin/stdout using JSON‑RPC messages and exposes an llm_call tool that accepts a prompt (required) and optionally a model override.
llm-accounting for logging, rate limiting, and audit.It wraps the OpenRouter API into the MCP protocol, enabling any MCP‑compatible agent to use OpenRouter models through a standardized interface (stdin/stdout JSON‑RPC) and adds integrated logging/auditing via `llm-accounting`.
You must set the `OPENROUTER_API_KEY` environment variable (in a `.env` file). The API base URL and model can be overridden via CLI arguments (e.g., `--model` or `--llm-api-base-url`) or environment variables.
No. It is an STDIO server that communicates exclusively via standard input and output; it does not open a network port for MCP communication.
Any model available through OpenRouter.ai. The default model is `perplexity/llama-3.1-sonar-small-128k-online`, but you can specify any OpenRouter model via the `--model` CLI argument or per‑call in the `llm_call` tool’s arguments.
Yes. The server integrates with `llm-accounting` for robust logging, rate limiting, and audit functionality, enabling monitoring of remote LLM usage, inference costs, and inspection of queries/responses.
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