MCP Chat Adapter

MCP Chat Adapter is a lightweight Model Context Protocol (MCP) server that turns any OpenAI‑compatible chat completion API into a first‑class tool for large language models. It solves the common pain point of having to write bespoke integration code for each chat‑based model: developers can now treat the server as a single, well‑defined MCP endpoint that accepts and returns chat messages, conversation identifiers, and tool calls in a standard format.

The server’s core value lies in its conversation‑centric design. When an LLM client, such as Claude, initiates a chat session it can either create a brand‑new conversation or resume an existing one by supplying a . All state—including message history, system prompts, and model parameters—is persisted locally in a configurable directory. This persistence allows long‑running assistants to maintain context across restarts, share conversations with multiple users, or even manually edit history for debugging and fine‑tuning. The MCP interface abstracts away the underlying HTTP calls, rate limits, and error handling, giving developers a clean API surface that mirrors the natural chat flow of modern LLMs.

Key capabilities include:

• Tool‑based conversation management: Create, retrieve, and edit conversations through dedicated MCP tools.
• Model flexibility: Configure default models, system prompts, and generation parameters on a per‑conversation basis, with sensible fallbacks.
• Robust error handling: Automatic timeouts and retry logic keep the client side free from low‑level network concerns.
• OpenAI compatibility: Works with any service that exposes the OpenAI chat completion endpoint, including custom bases such as openrouter.ai or local deployments.
• FastMCP foundation: Built on the FastMCP framework, ensuring high performance and easy extensibility.

In real‑world scenarios this server shines for developers building AI‑powered applications that require persistent, multi‑turn dialogue. For example, a customer support chatbot can maintain separate conversation threads for each ticket, allowing the assistant to pick up where it left off even after a server reboot. A research lab can store thousands of experimental conversations, automatically tagging and querying them later for analysis. Because the MCP contract is language‑agnostic, any LLM—Claude, GPT‑4, Gemini, or a custom model—can interact with the same backend without modification.

Integrating MCP Chat Adapter into an AI workflow is straightforward: configure environment variables for API keys, base URLs, and storage paths; launch the server via ; then reference the server name () in your LLM’s tool list. From there, the client simply calls , , or as needed, and the server handles all API plumbing. This decouples application logic from provider specifics, giving developers a reusable, battle‑tested component that can be swapped out or upgraded with minimal friction.