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Showing content from https://github.com/aiamblichus/mcp-chat-adapter below:

aiamblichus/mcp-chat-adapter: MCP server for using OpenAI compatible chat endpoints

An MCP (Model Context Protocol) server that provides a clean interface for LLMs to use chat completion capabilities through the MCP protocol. This server acts as a bridge between an LLM client and any OpenAI-compatible API. The primary use case is for chat models, as the server does not provide support for text completions.

The OpenAI Chat MCP Server implements the Model Context Protocol (MCP), allowing language models to interact with OpenAI's chat completion API in a standardized way. It enables seamless conversations between users and language models while handling the complexities of API interactions, conversation management, and state persistence.

• Built with FastMCP for robust and clean implementation
• Provides tools for conversation management and chat completion
• Proper error handling and timeouts
• Supports conversation persistence with local storage
• Easy setup with minimal configuration
• Configurable model parameters and defaults
• Compatible with OpenAI and OpenAI-compatible APIs

The idea is that you can have Claude spin off and maintain multiple conversations with other models in the background. All conversations are stored in the CONVERSATION_DIR directory, which you should set in the env section of your mcp.json file.

It is possible to tell Claude either to create a new conversation, or to continue an existing one (identified by the integer conversation_id). You can continue with the old conversation even if you are starting fresh in a new context, although in that case you may want to tell Claude to read the old conversation before continuing using the get_conversation tool.

Note that you can also edit the conversations in the CONVERSATION_DIR directory manually. In this case, you may need to restart the server to see the changes.

These environment variables must be set for the server to function:

OPENAI_API_KEY=your-api-key  # Your API key for OpenAI or compatible service
OPENAI_API_BASE=https://openrouter.ai/api/v1 # The base URL for the API (can be changed for compatible services)

You should also set the CONVERSATION_DIR environment variable to the directory where you want to store the conversation data. Use an absolute path.

The following environment variables are optional and have default values:

# Model Configuration
DEFAULT_MODEL=google/gemini-2.0-flash-001 # Default model to use if not specified
DEFAULT_SYSTEM_PROMPT="You are an unhelpful assistant."  # Default system prompt
DEFAULT_MAX_TOKENS=50000 # Default maximum tokens for completion
DEFAULT_TEMPERATURE=0.7  # Default temperature setting
DEFAULT_TOP_P=1.0 # Default top_p setting
DEFAULT_FREQUENCY_PENALTY=0.0 # Default frequency penalty
DEFAULT_PRESENCE_PENALTY=0.0  # Default presence penalty

# Storage Configuration
CONVERSATION_DIR=./convos # Directory to store conversation data
MAX_CONVERSATIONS=1000 # Maximum number of conversations to store

Your mcp.json file should look like this:

{
  "mcpServers": {
    "chat-adapter": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-chat-adapter"
      ],
      "env": {
          "CONVERSATION_DIR": "/Users/aiamblichus/mcp-convos",
          "OPENAI_API_KEY": "xoxoxo",
          "OPENAI_API_BASE": "https://openrouter.ai/api/v1",
          "DEFAULT_MODEL": "qwen/qwq-32b"
      }
    }
  }
}

The latest version of the package is published to npm here.

Creates a new chat conversation.

{
  "name": "create_conversation",
  "arguments": {
    "model": "gpt-4",
    "system_prompt": "You are a helpful assistant.",
    "parameters": {
      "temperature": 0.7,
      "max_tokens": 1000
    },
    "metadata": {
      "title": "My conversation",
      "tags": ["important", "work"]
    }
  }
}

Adds a message to a conversation and gets a response.

{
  "name": "chat",
  "arguments": {
    "conversation_id": "123",
    "message": "Hello, how are you?",
    "parameters": {
      "temperature": 0.8
    }
  }
}

Gets a list of available conversations.

{
  "name": "list_conversations",
  "arguments": {
    "filter": {
      "tags": ["important"]
    },
    "limit": 10,
    "offset": 0
  }
}

Gets the full content of a conversation.

{
  "name": "get_conversation",
  "arguments": {
    "conversation_id": "123"
  }
}

Deletes a conversation.

{
  "name": "delete_conversation",
  "arguments": {
    "conversation_id": "123"
  }
}

# Clone the repository
git clone https://github.com/aiamblichus/mcp-chat-adapter.git
cd mcp-chat-adapter

# Install dependencies
yarn install

# Build the project
yarn build

For FastMCP cli run:

For FastMCP inspect run:

Contributions are welcome! Please feel free to submit a Pull Request.

MIT

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