Openai Codex MCP

🚀 OpenAI Codex Documentation

OpenAI Codex is a powerful tool that can automatically generate high - quality programming code, explain existing code, and debug code issues. This documentation will guide you through the installation, configuration, and usage of OpenAI Codex.

🚀 Quick Start

📦 Installation

pip install openai-codex

⚙️ Configuration

Create a .env file in the root directory of your project:

CODEX_API_KEY=your_api_key_here

💻 Usage Examples

Basic Usage

Initialize the Codex client and generate code:

from openai.codex import Codex

client = Codex(api_key="your_api_key_here")

response = client.chat.completions.create(
    model="code-davinci-002",
    messages=[{
        "role": "user",
        "content": "Write a Python function to calculate the Fibonacci sequence."
    }]
)
print(response.choices[0].message.content)

✨ Features

Main Features

• Code Generation: Automatically generate high - quality programming code.
• Code Explanation: Analyze in - depth how existing code works.
• Error Debugging: Identify and fix issues in the code.

Supported Languages

• Python
• JavaScript
• Java
• C++
• Ruby
• Go

📚 Documentation

Code Generation (write_code)

response = client.write_code(
    task="Write a function to sort an array using quicksort algorithm.",
    language="python"
)
print(response.content)

Code Explanation (explain_code)

response = client.explain_code(
    code="""
        def quicksort(arr):
            if len(arr) <= 1:
                return arr
            pivot = arr[len(arr)//2]
            less = [x for x in arr if x < pivot]
            equal = [x for x in arr if x == pivot]
            greater = [x for x in arr if x > pivot]
            return quicksort(less) + equal + quicksort(greater)
    """
)
print(response.explanation)

Code Debugging (debug_code)

response = client.debug_code(
    code="""
        def fibonacci(n):
            if n <= 0:
                return []
            elif n == 1:
                return [1]
            sequence = [1, 1]
            for i in range(2, n):
                sequence.append(sequence[i-2] + sequence[i-1])
            return sequence
    """,
    issue_description="The function returns one extra Fibonacci number."
)
print(response.debug_info)

🔧 Technical Details

Reasoning Models

• code-davinci-002: A multi - purpose code reasoning model.
• code-cushman-001: An efficient code generation auxiliary model.

GPT Models

• gpt-4: A general - purpose large language model.
• gpt-3.5-turbo: An enhanced version of the GPT - 3.5 model.

💻 Usage Examples

Using the write_code Method

User: Can you write a Python function to generate prime numbers using OpenAI Codex?

Claude: I will use the `write_code` method of OpenAI Codex to generate this function.
[Claude will call the `write_code` method, specifying the task and language]

Using the explain_code Method

User: Can you explain how this quicksort algorithm works?

Claude: I will use OpenAI Codex to explain this algorithm.
[Claude will call the `explain_code` method and provide the code]

Using the debug_code Method

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

Claude: I will let OpenAI Codex find and fix the bug in the code.
[Claude will call the `debug_code` method and provide the code and problem description]

🎯 Model Specification

User: Please use the `code-davinci-002` model to generate code.

Claude: Okay, I will use the `code-davinci-002` model.
[Claude will call the corresponding API and specify the model parameter]

By following the above steps and methods, you can fully experience the powerful functions of OpenAI Codex and improve your development efficiency.