openai-codex-mcp

An MCP server to wrap the OpenAI Codex CLI tool for use with Claude Code.

Overview

This project provides a simple JSON-RPC server that allows Claude Code to interact with the OpenAI Codex CLI tool. This enables Claude Code to use OpenAI's models for code generation, explanation, and problem-solving when needed.

Video Demo

https://www.loom.com/share/5d9532a79ae24b309af08c1727156f9c?sid=d9277b91-bf8c-43ec-a3c6-dc69fc52e1d2

Recent Improvements

The MCP server has been enhanced to provide a more intuitive and robust interface:

1. Specialized Methods: Added dedicated methods for common coding tasks:

2. Model Selection: Clearly defined model options with defaults:

3. Simplified Syntax: More intuitive parameter naming and structure for easier integration

These improvements make it easier for Claude Code to use OpenAI's models for specific programming tasks without requiring complex prompt engineering.

Prerequisites

• Python 3.12+
• OpenAI Codex CLI tool (`npm install -g @openai/codex`)
• Valid OpenAI API key (for the Codex CLI, not for this server)

Installation and Setup

This project uses a PEP‑621 `pyproject.toml`. Follow these steps:

# 1. Create & activate a venv
uv venv                     # creates a .venv/ directory
source .venv/bin/activate   # on Windows: .\.venv\Scripts\activate

# 2. Install package and dependencies into the venv
uv pip install .

After installation, the `codex_server` entrypoint is available in your PATH.

Running the Server

Quick Start

Use the provided setup script to automatically set up the environment and start the server:

./setup_and_run.sh

This script will:

• Check for the `codex` CLI installation
• Set up a Python virtual environment if needed
• Install the MCP tool via the Claude CLI if available
• Start the MCP server

Manual Start

If you prefer to start the server manually:

1. Make sure the Codex CLI is installed and properly configured with your OpenAI API key

2. Start the server:

codex_server

*Alternatively, use uvicorn directly:* `uvicorn codex_server:app`

Integrating with Claude Code

Once your MCP server is running, you can register it with Claude Code using either of these methods:

Method 1: Using the Claude CLI (Recommended)

The repository includes a configuration file that can be installed directly using the Claude CLI:

# Install the MCP tool directly from the JSON config
claude mcp add /path/to/openai_codex_mcp.json

# Verify the tool was installed correctly
claude mcp list

Method 2: Manual Configuration via UI

Alternatively, you can register the tool manually:

1. In Claude Code, navigate to Settings → Tools → Manage MCP Tools.

2. Create a new tool with:

3. Save the tool.

Now, Claude Code can use the OpenAI Codex CLI tool for tasks where a different perspective or approach is desired. You can invoke it by asking Claude to use the OpenAI models for a particular task.

API Usage

The MCP server provides multiple methods to interact with OpenAI's models for different coding tasks.

Method 1: General Completion

For flexible, custom prompts to OpenAI:

curl -X POST http://localhost:8000/ \
     -H 'Content-Type: application/json' \
     -d '{
       "jsonrpc": "2.0",
       "method": "codex_completion",
       "params": {
         "prompt": "Write a JavaScript function to sort an array of objects by a property value",
         "model": "o4-mini"
       },
       "id": 1
     }'

Method 2: Write Code

Specialized method for code generation with language specification:

curl -X POST http://localhost:8000/ \
     -H 'Content-Type: application/json' \
     -d '{
       "jsonrpc": "2.0",
       "method": "write_code",
       "params": {
         "task": "Calculate the first 100 Fibonacci numbers and return them as an array",
         "language": "python",
         "model": "o4-mini"
       },
       "id": 1
     }'

Method 3: Explain Code

Specialized method for code explanation:

curl -X POST http://localhost:8000/ \
     -H 'Content-Type: application/json' \
     -d '{
       "jsonrpc": "2.0",
       "method": "explain_code",
       "params": {
         "code": "def quicksort(arr):\n    if len(arr)  pivot]\n    return quicksort(left) + middle + quicksort(right)",
         "model": "o4-mini"
       },
       "id": 1
     }'

Method 4: Debug Code

Specialized method for finding and fixing bugs:

curl -X POST http://localhost:8000/ \
     -H 'Content-Type: application/json' \
     -d '{
       "jsonrpc": "2.0",
       "method": "debug_code",
       "params": {
         "code": "function fibonacci(n) {\n  if (n <= 0) return [];\n  if (n === 1) return [1];\n  let sequence = [1, 1];\n  for (let i = 2; i <= n; i++) {\n    sequence.push(sequence[i-2] + sequence[i-1]);\n  }\n  return sequence;\n}",
         "issue_description": "It generates one too many Fibonacci numbers",
         "model": "o4-mini"
       },
       "id": 1
     }'

Available Models

Reasoning Models (O-series)

• `o4-mini`: Faster, more affordable reasoning model
• `o3`: Most powerful reasoning model
• `o3-mini`: A small model alternative to o3
• `o1`: Previous full o-series reasoning model
• `o1-mini`: A small model alternative to o1
• `o1-pro`: Version of o1 with more compute for better responses

GPT Models

• `gpt-4.1`: Flagship GPT model for complex tasks
• `gpt-4o`: Fast, intelligent, flexible GPT model
• `gpt-4.1-mini`: Balanced for intelligence, speed, and cost
• `gpt-4.1-nano`: Fastest, most cost-effective GPT-4.1 model
• `gpt-4o-mini`: Fast, affordable small model for focused tasks

Available Parameters

General Completion (codex_completion)

• `prompt` (required): The prompt to send to Codex
• `model` (optional): The model to use (e.g., "o4-mini", "o3", "gpt-4.1", "gpt-4o-mini")
• `images` (optional): List of image paths or data URIs to include
• `additional_args` (optional): Additional CLI arguments to pass to Codex

Write Code (write_code)

• `task` (required): Description of the coding task
• `language` (required): Programming language for the solution (e.g., "python", "javascript", "java")
• `model` (optional): The model to use

Explain Code (explain_code)

• `code` (required): The code to explain
• `model` (optional): The model to use

Debug Code (debug_code)

• `code` (required): The code to debug
• `issue_description` (optional): Description of the issue or error
• `model` (optional): The model to use

Example Usage with Claude Code

Once configured, you can ask Claude to use OpenAI Codex for specific tasks using any of the available methods:

Using write_code Method

User: Can you use OpenAI Codex to write a Python function that generates prime numbers?

Claude: I'll use the OpenAI Codex write_code method to generate that function.

[Claude would use the write_code method with task="Write a Python function that generates prime numbers" and language="python"]

Using explain_code Method

User: Can you ask OpenAI Codex to explain how this quicksort algorithm works?

Claude: I'll use OpenAI Codex to explain this algorithm.

[Claude would use the explain_code method with the code provided]

Using debug_code Method

User: My JavaScript function has a bug. Can you use OpenAI Codex to debug it?

Claude: I'll ask OpenAI Codex to find and fix the bug in your code.

[Claude would use the debug_code method with the code provided]

Specifying a Model

User: Can you use OpenAI Codex with the o4-preview model to write an efficient implementation of a binary search tree?

Claude: I'll use the o4-preview model to generate that implementation.

[Claude would use the specified model with the appropriate method]

This allows you to leverage both Claude and OpenAI's capabilities seamlessly within the same interface, with specialized methods for common coding tasks.