About OpenAI MCP Server
An MCP server that lets AI assistants use OpenAI models for text generation and image creation, with tools to inspect available models and configure generation settings.
Key features
- 01Chat completion across OpenAI models
- 02List available models
- 03Configure generation parameters
- 04Generate images with gpt-image-1
Use cases
- 01Ask ChatGPT to review coding agent output
- 02Generate images from text prompts
- 03Select OpenAI models for different tasks
MCP support
Exposes chat_completion, list_models, and generate_image tools to MCP clients, connecting requests to the OpenAI API.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEOpenAI MCP Server
A Model Context Protocol (MCP) server that enables AI assistants to interact with OpenAI's ChatGPT API.
Features
- Chat Completion: Generate text using OpenAI's latest language models (GPT-4.1, GPT-4.1-mini, GPT-4.1-nano, GPT-4o, o1, o1-pro, o3, o4-mini)
- List Models: Retrieve available OpenAI models
- Configurable Parameters: Control temperature, max tokens, and other generation settings
Prerequisites
- Node.js 18 or higher
- OpenAI API key
Installation
# Clone the repository
git clone https://github.com/akiojin/openai-mcp-server.git
cd openai-mcp-server
# Install dependencies
npm install
# Build the project
npm run build
Configuration
- Create a
.envfile in the project root:
cp .env.example .env
- Add your OpenAI API key to the
.envfile:
OPENAI_API_KEY=your_api_key_here
Usage
使用例
このMCPサーバーは、Claude CodeなどのAIアシスタントから呼び出して使用することを想定しています:
- 出力結果の検証: Claude Codeの出力結果をChatGPTに検証させることができます
- 画像生成: Claude Codeでは直接画像生成ができませんが、このMCPサーバー経由でChatGPTのgpt-image-1モデルを使用した画像生成が可能です
- 異なるモデルの活用: 用途に応じて様々なOpenAIモデル(GPT-4.1、o1、o3など)を使い分けることができます
As an MCP Server
Add the server to your MCP client configuration:
{
"servers": {
"openai": {
"command": "node",
"args": ["path/to/openai-mcp-server/dist/index.js"]
}
}
}
Available Tools
chat_completion
Generate text using ChatGPT.
Parameters:
model(string, optional): Model to use (default: "gpt-4.1")messages(array, required): Chat history with role and contenttemperature(number, optional): Sampling temperature 0-2 (default: 0.7)- Note: GPT-5 models only support temperature=1
max_tokens(number, optional): Maximum tokens to generate- For GPT-4 models: defaults to 1000
- For GPT-5 models: optional - if not specified, model automatically allocates necessary tokens
reasoning_effort(string, optional): GPT-5 only - Controls reasoning depthminimal: Fast responses, no reasoning tokenslow: Light reasoningmedium: Standard reasoning (default)high: Deep reasoning for complex analysis
verbosity(string, optional): GPT-5 only - Response length (low/medium/high)
list_models
List all available OpenAI models.
generate_image
Generate images using gpt-image-1 model. Returns file paths for generated images saved in temporary directory.
Parameters:
prompt(string, required): Text description of the desired image(s). Maximum 1000 characters.model(string, optional): Model to use (default: "gpt-image-1")n(number, optional): Number of images to generate 1-10 (default: 1)size(string, optional): Size of images - "1024x1024", "1024x1536", "1536x1024", or "auto" (default: "1024x1024")quality(string, optional): Quality - "low", "medium", "high", or "auto" (default: "auto")background(string, optional): Background type - "opaque" or "transparent" (default: "opaque")
Note: Generated images are saved as temporary files to minimize token usage. The response includes file paths instead of base64 data.
Example:
{
"prompt": "A beautiful sunset over mountains",
"size": "1024x1024",
"quality": "high",
"background": "opaque"
}
Development
# Run in development mode with hot reload
npm run dev
# Build for production
npm run build
# Watch mode for development
npm run watch
Project Structure
openai-mcp-server/
├── src/
│ └── index.ts # Main server implementation
├── dist/ # Compiled JavaScript (generated)
├── .env # Environment variables (create from .env.example)
├── .env.example # Example environment configuration
├── package.json # Project dependencies and scripts
├── tsconfig.json # TypeScript configuration
└── README.md # This file
License
MIT License - see LICENSE file for details
Release Process
This project uses semantic-release for automated versioning and publishing.
Commit Message Format
Follow Conventional Commits format:
<type>(<scope>): <description>
[optional body]
[optional footer(s)]
Commit Types
feat:new feature (minor version bump)fix:bug fix (patch version bump)BREAKING CHANGE:breaking change (major version bump)docs:documentation changes (patch version bump)style:formatting changes (patch version bump)refactor:code refactoring (patch version bump)test:adding tests (patch version bump)chore:maintenance tasks (patch version bump)
Examples
# New feature (0.1.0 → 0.2.0)
feat: add streaming support for chat completion
# Bug fix (0.1.0 → 0.1.1)
fix: handle connection timeout properly
# Breaking change (0.1.0 → 1.0.0)
feat: redesign API interface
BREAKING CHANGE: The chat_completion tool now requires a different parameter structure
Helper Scripts
# Interactive commit with commitizen
npm run commit
# Test release without publishing
npm run release:dry-run
# Manual release (usually handled by CI)
npm run release
Contributing
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Follow conventional commit format for your commits
- Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Support
For issues and feature requests, please use the GitHub issue tracker.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.