mcp-openai-complete

mcp-openai-complete

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ai_chatbot

The OpenAI Complete MCP Server is designed to bridge LLM clients with OpenAI-compatible APIs via the MCP protocol. It focuses on text completion and handles asynchronous processes and request cancellations. Standout features include efficient handling and configuration through environment variables or Docker.

OpenAI Complete MCP Server

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

Features

  • Provides a single tool named "complete" for generating text completions
  • Properly handles asynchronous processing to avoid blocking
  • Implements timeout handling with graceful fallbacks
  • Supports cancellation of ongoing requests

Installation

# Clone the repository
git clone <repository-url>
cd mcp-openai-complete

# Install dependencies
pnpm install

# Build the project
pnpm run build

Configuration

The following environment variables are required:

OPENAI_API_KEY=your-hyperbolic-api-key
OPENAI_API_BASE=https://api.hyperbolic.xyz/v1
OPENAI_MODEL=meta-llama/Meta-Llama-3.1-405B

Usage

Start the server:

pnpm start

This will start the server on stdio, making it available for MCP clients to communicate with.

Docker Usage

Building the Docker Image

docker build -t mcp-openai-complete .

Running the Container

# Run with environment variables
docker run -it --rm \
  -e OPENAI_API_KEY="your-api-key" \
  -e OPENAI_MODEL="gpt-3.5-turbo-instruct" \
  mcp-openai-complete

You can also use a .env file:

# Run with .env file
docker run -it --rm \
  --env-file .env \
  mcp-openai-complete

Parameters for the "complete" tool

  • prompt (string, required): The text prompt to complete
  • max_tokens (integer, optional): Maximum tokens to generate, default: 150
  • temperature (number, optional): Controls randomness (0-1), default: 0.7
  • top_p (number, optional): Controls diversity via nucleus sampling, default: 1.0
  • frequency_penalty (number, optional): Decreases repetition of token sequences, default: 0.0
  • presence_penalty (number, optional): Increases likelihood of talking about new topics, default: 0.0

Development

For development with auto-reloading:

npm run dev

License

MIT