openai-plugins

openai-mcp

A TypeScript library that provides an OpenAI-compatible client for the Model Context Protocol (MCP).

Installation

npm install openai-mcp

Features

• OpenAI API compatibility - works as a drop-in replacement for the OpenAI client
• Connects to local or remote Model Context Protocol servers
• Supports tool use and function calling
• Rate limiting and retry logic built in
• Configurable logging
• TypeScript type definitions included

Usage

import { OpenAI } from 'openai-mcp';

// Create an OpenAI-compatible client connected to an MCP server
const openai = new OpenAI({
  mcp: {
    // MCP server URL(s) to connect to
    serverUrls: ['http://localhost:3000/mcp'],
    
    // Optional: set log level (debug, info, warn, error)
    logLevel: 'info',
    
    // Additional configuration options
    // modelName: 'gpt-4',             // Default model to use
    // disconnectAfterUse: true,       // Auto-disconnect after use
    // maxToolCalls: 15,               // Max number of tool calls per conversation
    // toolTimeoutSec: 60,             // Timeout for tool calls
  }
});

// Use the client like a standard OpenAI client
const response = await openai.chat.completions.create({
  model: 'gpt-4',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Hello, how are you today?' }
  ]
});

console.log(response.choices[0].message.content);

Logging Configuration

import { setMcpLogLevel } from 'openai-mcp';

// Set log level to one of: 'debug', 'info', 'warn', 'error'
setMcpLogLevel('info');

Environment Variables

The library also supports configuration through environment variables:

# MCP Server URL(s) - comma separated for multiple servers
MCP_SERVER_URL=http://localhost:3000/mcp

# API Keys for different model providers
OPENAI_API_KEY=your-openai-api-key
ANTHROPIC_API_KEY=your-anthropic-api-key
GEMINI_API_KEY=your-gemini-api-key

Multi-Model Support

The library supports routing requests to different model providers based on the model name:

import { OpenAI } from 'openai-mcp';

const openai = new OpenAI();

// Uses OpenAI API
const gpt4Response = await openai.chat.completions.create({
  model: 'gpt-4',
  messages: [{ role: 'user', content: 'Hello GPT-4' }]
});

// Uses Anthropic API
const claudeResponse = await openai.chat.completions.create({
  model: 'claude-3',
  messages: [{ role: 'user', content: 'Hello Claude' }]
});

// Uses Google Gemini API
const geminiResponse = await openai.chat.completions.create({
  model: 'gemini-pro',
  messages: [{ role: 'user', content: 'Hello Gemini' }]
});

Examples

The examples/ directory contains various usage examples:

• Basic Usage: Simple chat completion request
• Streaming: Stream responses token by token
• Multi-Model: Use OpenAI, Anthropic, and Gemini models
• Tools Usage: Function/tool calling with MCP
• Custom Logging: Configure and use the logging system

See the Examples README for more details on running these examples.

Development

To build the library:

npm run build

To run tests:

npm test