Wrap a call to any remote LLM model and expose it as an MCP server tool to allow your main model to communicate with other models.
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# LLM Wrapper MCP Server > "Allow any MCP-capable LLM agent to communicate with or delegate tasks to any other LLM available through the OpenRouter.ai API." A Model Context Protocol (MCP) server wrapper designed to facilitate seamless interaction with various Large Language Models (LLMs) through a standardized interface. This project enables developers to integrate LLM capabilities into their applications by providing a robust and flexible server that handles LLM calls, tool execution, and result processing. ## Features - Implements the Model Context Protocol (MCP) specification for standardized LLM interactions. - Provides a FastAPI-based server for handling LLM requests and responses. - Supports advanced features like tool calls and results through the MCP protocol. - Configurable to use various LLM providers (e.g., OpenRouter, local models). - Designed for extensibility, allowing easy integration of new LLM backends. - Integrates with `llm-accounting` for robust logging, rate limiting, and audit functionalities, enabling monitoring of remote LLM usage, inference costs, and inspection of queries/responses for debugging or legal purposes. ## Dependencies This project relies on the following key dependencies: ### Core Dependencies: * `fastapi`: A modern, fast (high-performance) web framework for building APIs with Python 3.7+. * `uvicorn`: An ASGI server, used to run FastAPI applications. * `pydantic`: Data validation and settings management using Python type hints. * `pydantic-settings`: Pydantic's settings management for environment variables and configuration. * `python-dotenv`: Reads key-value pairs from a `.env` file and sets them as environment variables. * `requests`: An elegant and simple HTTP library for Python. * `tiktoken`: A fast BPE tokeniser for use with OpenAI's models. * `llm_accounting`: For robust logging, rate limiting, and audit functionalities, enabling monitoring of remote LLM usage, inference costs, and inspection of qu…
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