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OpenAI Codex MCP Server

OpenAI Codex MCP Server

Local
github.com/Tomatio13AI & Agents4Updated 74w ago

About OpenAI Codex MCP Server

An MCP server that wraps the OpenAI Codex CLI for use from Claude Code and other MCP clients. It exposes coding tasks, interactive sessions, image input, and configurable automation levels.

Key features

  1. 01Code generation, explanation, and debugging
  2. 02Refactoring, tests, and documentation
  3. 03Image input support
  4. 04Multiple AI providers
  5. 05Three approval modes
  6. 06Interactive sessions

Use cases

  1. 01Generate or fix code
  2. 02Check code for security issues
  3. 03Implement a UI from an image
  4. 04Start an interactive coding session

MCP support

Exposes OpenAI Codex CLI capabilities through MCP tools, including codex_agent and codex_interactive. Supports stdio and SSE transports for MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

OpenAI Codex MCP Server

Python Version MCP Protocol OpenAI Codex License

JA doc EN doc

Wraps OpenAI Codex CLI as an MCP (Model Context Protocol) server for integration with Claude.

🚀 Features

This MCP server provides the following tools:

  • codex_agent: Comprehensive tool with access to all OpenAI Codex CLI features

    • Code generation, explanation, debugging, refactoring
    • Security analysis, test creation, documentation generation
    • Multimodal support (image input)
    • Multiple AI provider support
    • 3-level automation modes (suggest/auto-edit/full-auto)
  • codex_interactive: Tool for starting interactive sessions

🔌 Supported Modes

  • stdio mode: Uses standard input/output (for Claude Desktop)
  • SSE mode: Uses Server-Sent Events (for Web API)

📋 Prerequisites

  1. OpenAI Codex CLI:

    npm install -g @openai/codex
    
  2. Python 3.12 or higher

  3. Environment setup: Copy .env.example to .env and configure

🛠️ Installation

1. Clone the repository
git clone https://github.com/Tomatio13/openai-codex-mcp.git
cd openai-codex-mcp
2. Setup
./setup.sh
3. Start the server
stdio mode (for Claude Desktop)
./run.sh
SSE mode (for Web API)
./run_sse.sh [port] [host]

# Examples:
./run_sse.sh 8080 0.0.0.0  # Port 8080, listen on all interfaces
./run_sse.sh              # Default: localhost:8000
Manual startup
# stdio mode
python codex_server.py --mode stdio

# SSE mode
python codex_server.py --mode sse --port 8000 --host localhost

Or run setup and start in one command:

./setup_and_run.sh
Manual installation
# Install dependencies
pip install -e .

# Start server
python codex_server.py

🔧 Claude Configuration

stdio mode (recommended)

To use the MCP server with Claude Desktop, add the following to your configuration file:

macOS

~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "openai-codex": {
      "command": "python",
      "args": ["/path/to/openai-codex-mcp/codex_server.py", "--mode", "stdio"],
      "env": {
        "PATH": "/usr/local/bin:/usr/bin:/bin"
      }
    }
  }
}
Windows

%APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "openai-codex": {
      "command": "python",
      "args": ["C:\\path\\to\\openai-codex-mcp\\codex_server.py", "--mode", "stdio"],
      "env": {
        "PATH": "C:\\Program Files\\nodejs;C:\\Windows\\System32"
      }
    }
  }
}
SSE mode

In SSE mode, you can access the MCP server via HTTP endpoints:

# Start server
./run_sse.sh 8000

# Available endpoints
# GET  http://localhost:8000/sse - SSE connection
# POST http://localhost:8000/message - Send message

💡 Usage

Once the MCP server is configured in Claude Desktop, you can use the tools as follows:

Basic code generation
@openai-codex codex_agent prompt="Create a Python function to calculate Fibonacci numbers"
Task-specific optimization
@openai-codex codex_agent prompt="Explain this code" task_type="code-explanation"
@openai-codex codex_agent prompt="Fix the bug in utils.py" task_type="debugging" model="o4-preview"
@openai-codex codex_agent prompt="Check for security vulnerabilities" task_type="security"
Automation level settings
# Suggestions only (default, safest)
@openai-codex codex_agent prompt="Refactor this code" approval_mode="suggest"

# Auto-edit (automatic file read/write, command execution requires confirmation)
@openai-codex codex_agent prompt="Explain this codebase" approval_mode="auto-edit"

# Full auto (fully automatic execution in network-disabled sandbox)
@openai-codex codex_agent prompt="Create and run tests" approval_mode="full-auto"
Multimodal (image input)
@openai-codex codex_agent prompt="Implement this UI design" images=["design.png"] task_type="code-generation"
Using different AI providers
@openai-codex codex_agent prompt="Review this code" provider="azure" model="gpt-4.1"
@openai-codex codex_agent prompt="Explain this" provider="ollama" model="llama3"
Interactive sessions
@openai-codex codex_interactive initial_prompt="Tell me about the project structure" approval_mode="auto-edit"

🎯 Task Types

Use the task_type parameter to generate prompts optimized for specific tasks:

  • general: General coding assistance (default)
  • code-generation: Generate new code
  • code-explanation: Explain existing code
  • debugging: Find and fix bugs
  • refactoring: Improve code structure
  • testing: Write or fix tests
  • security: Security analysis and fixes
  • documentation: Generate or improve documentation

🤖 AI Providers

Supported AI providers through the provider parameter:

  • openai: OpenAI (default)
  • azure: Azure OpenAI
  • gemini: Google Gemini
  • ollama: Ollama (local models)
  • mistral: Mistral AI
  • deepseek: DeepSeek
  • xai: xAI
  • groq: Groq

🔒 Approval Modes

  • suggest: Only suggests changes, requires approval for all actions (safest)
  • auto-edit: Can read and write files automatically, asks for shell commands
  • full-auto: Full autonomy with network-disabled sandbox (most powerful)

🛡️ Security

  • The full-auto mode runs in a network-disabled sandbox for security
  • File operations are limited to the current working directory
  • All shell commands require explicit approval unless in full-auto mode

🐛 Troubleshooting

Common Issues
  1. "codex command not found"

    npm install -g @openai/codex
    
  2. Permission denied on scripts

    chmod +x *.sh
    
  3. Python version issues

    • Ensure Python 3.12+ is installed
    • Use virtual environment: python3 -m venv venv && source venv/bin/activate
  4. MCP connection issues

    • Check Claude Desktop configuration file syntax
    • Verify file paths are absolute
    • Restart Claude Desktop after configuration changes

📝 License

MIT License with Attribution - see LICENSE file for details.

🤝 Contributing

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

📞 Support

If you encounter any issues or have questions, please open an issue on GitHub.


Made with ❤️ for the Claude + OpenAI Codex community

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