iflow-mcp_jeanchristophe13v_codex-mcp-async 0.2.1

Asynchronous MCP wrapper for OpenAI Codex CLI with context filtering

Codex MCP Async Server

Asynchronous MCP wrapper for OpenAI Codex CLI with 95% context savings

Enable Claude Code to call Codex (GPT-5) asynchronously, filtering out thinking processes to save 95% context tokens.

Features

• ✅ Async execution - Start Codex tasks in background, continue working
• ✅ Context-efficient - Filters thinking/exec logs, returns only core results
• ✅ Full control - Access all Codex models and reasoning efforts
• ✅ Zero config - Works out of the box with Claude Code

Quick Start

🚀 Install with UVX

Zero configuration - just run:

uvx codex-mcp-async

Configure Claude Code

Add to your ~/.claude/settings.json:

{
  "mcpServers": {
    "codex-mcp": {
      "command": "uvx",
      "args": ["codex-mcp-async"],
      "env": {}
    }
  }
}

Or use the provided config: Copy mcp-config.json to your Claude settings directory

Restart Claude Code

Reload or restart Claude Code to load the MCP server.

Usage Examples

🚀 Async Execution (Game Changer!)

Start a long Codex task and continue working immediately:

You:

Please analyze this entire codebase and generate comprehensive documentation

Claude: I'll analyze your entire codebase and generate comprehensive documentation. This is a large task, so I'll start it asynchronously...

✅ Task Started (Task ID: abc12345) You can continue working on other things while it runs in the background!

You: (Continue working immediately)

While the documentation is being generated, help me fix this bug

Claude: Sure! Let me help you fix that bug...

You: (A few minutes later)

Can you check if the documentation task is finished?

Claude: ✅ Documentation Complete!

[View Generated Documentation]

• API reference
• Code examples
• Architecture overview
• Setup instructions

⚡ Parallel Execution

Run multiple Codex tasks simultaneously:

You:

I need to do three things at once:

1. Generate unit tests for utils.py
2. Refactor database.py to use async/await
3. Add type hints to all functions in api.py

Claude: I'll start all three tasks in parallel!

🔄 Task 1 Started (Task ID: task1) - Generating unit tests 🔄 Task 2 Started (Task ID: task2) - Refactoring database code 🔄 Task 3 Started (Task ID: task3) - Adding type hints

All tasks are running in parallel...

You: (Later)

Are all three tasks finished?

Claude: ✅ All Complete!

• ✅ Task 1: Unit tests for utils.py generated
• ✅ Task 2: database.py refactored to async mode
• ✅ Task 3: Type hints added to api.py functions

🎯 Quick Sync Tasks

For simple immediate tasks:

You:

Write a Python function to validate email addresses

Claude:

import re

def validate_email(email):
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    return re.match(pattern, email) is not None

# Usage examples
print(validate_email("[e-mail]"))  # True
print(validate_email("invalid-email"))    # False

✅ Task Complete!

🧠 Model Selection

You:

Analyze this research paper using GPT-5 with high reasoning

Claude:

# Uses analysis model instead of coding model
mcp__codex_mcp__codex_execute(
    prompt="Analyze this research paper on machine learning",
    args=["--full-auto", "-m", "gpt-5", "--config", "model_reasoning_effort=high"]
)

Why Async + Context Filtering?

Problem 1: Codex blocks Claude Code while running. Problem 2: Codex outputs 3000+ tokens of thinking logs for every task.

Solution: This MCP server runs Codex asynchronously and filters out 95% of the noise.

Benefits:

• 🚀 Start a task and continue working immediately
• ⚡ Run multiple tasks in parallel
• 💾 95% context savings (3000 tokens → 150 tokens)
• 🎯 Clean, focused results only
• 🧹 Automatic process cleanup

Advanced Usage

Model Selection

gpt-5-codex (default) - Best for coding, debugging, implementation gpt-5 - Best for analysis, planning, research

Reasoning Levels

• minimal/low - Quick tasks
• medium - Standard work (default)
• high - Complex problems

Example Configurations

# Quick coding task
args=["--full-auto", "--config", "model_reasoning_effort=low"]

# Complex analysis
args=["--full-auto", "-m", "gpt-5", "--config", "model_reasoning_effort=high"]

# Web search + analysis
args=["--full-auto", "--search", "-m", "gpt-5"]

Architecture & Performance

Claude Code (you)
    ↓ calls MCP tool
codex-mcp-async (runs Codex in background)
    ↓ filters thinking logs (95% savings!)
Codex CLI (GPT-5)
    ↓ returns clean result
Claude Code (receives focused output)

Context Savings:

• Before: 3600 tokens (thinking + logs + result)
• After: 180 tokens (clean result only)
• 95% reduction!

Troubleshooting

Server not showing up?

• Check: uvx codex-mcp-async runs without errors
• Restart Claude Code after config change

Task stuck in "running"?

• Large tasks take time to complete
• Check debug logs: /tmp/codex_mcp_debug.log

Context too large?

• Enable filtering: Always use async mode for long tasks
• Split large tasks into smaller chunks

Requirements

• Python 3.8+
• Codex CLI installed and authenticated
• Claude Code
• uvx (for easy installation)

License

MIT License - see LICENSE

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Questions? Open an issue on GitHub.

Made with ❤️ for the Claude Code + Codex community