LLM Wrapper MCP Server

Overview

The LLM Wrapper MCP Server is a bridge that lets any Model Context Protocol (MCP)‑capable large language model (LLM) agent delegate work to any LLM exposed through the OpenRouter.ai API. By exposing a standard STDIO‑based interface, it abstracts away provider specifics and lets developers write agent code once while swapping backends at runtime. This solves the common pain point of having to rewrite or re‑configure agent logic whenever a new LLM provider is added, enabling rapid experimentation and deployment across multiple model families.

At its core, the server implements the MCP specification for request/response handling, tool invocation, and result reporting. It accepts a structured JSON payload from an agent, forwards the prompt to the chosen LLM via OpenRouter’s REST API (or any configured provider), and streams back the completed text or tool results. The integration with llm-accounting adds a layer of observability: every call is logged, rate‑limited, and auditable. This is invaluable for teams that need to track inference costs, enforce usage quotas, or review conversation history for compliance purposes.

Key capabilities include:

• Provider agnosticism: swap between OpenRouter, local models, or future APIs with minimal configuration changes.
• Tool execution support: the server can invoke external tools defined in the MCP specification, returning structured results to the agent.
• Extensibility: new backends can be added by extending the LLM client layer without touching the MCP handling logic.
• Robust monitoring: built‑in logging, rate limiting, and audit trails via llm-accounting.

Typical use cases span from building conversational assistants that need to switch between high‑capacity cloud models and on‑prem resources, to research pipelines where multiple LLMs are evaluated side‑by‑side. In a CI/CD environment, the server can be spun up as a container and integrated into automated testing suites that validate agent behavior against different backends. Because it communicates over STDIO, the server can be orchestrated by any language or runtime that supports process I/O, making it a versatile component in multi‑language AI ecosystems.