LLM Wrapper MCP Server

What is LLM Wrapper MCP Server?

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.

How to use LLM Wrapper MCP Server?

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.

Key features of LLM Wrapper MCP Server

• Implements the MCP specification for standardized LLM interactions.
• STDIO‑based server handling LLM requests and responses.
• Supports advanced tool calls and result processing via MCP.
• Configurable API base URL and model (OpenRouter by default).
• Designed for extensibility to integrate new LLM backends.
• Integrates with llm-accounting for logging, rate limiting, and audit.

Use cases of LLM Wrapper MCP Server

• Allow any MCP‑capable agent to delegate tasks to OpenRouter’s LLM models.
• Monitor and audit remote LLM usage, inference costs, and query/response payloads.
• Seamlessly swap or update LLM backends without changing agent code.
• Run a local MCP bridge that logs all LLM calls for debugging or compliance.

FAQ from LLM Wrapper MCP Server

What does LLM Wrapper MCP Server do that a direct API call doesn’t?

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.

How do I configure the server?

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.

Does the server open a network port?

No. It is an STDIO server that communicates exclusively via standard input and output; it does not open a network port for MCP communication.

What models can I use?

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.

Is logging and auditing included?

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.