Claude Code MCP Async Server

Asynchronous MCP wrapper for Claude Code CLI

Enable Claude Code to spawn child Claude Code sessions for parallel task execution.

Features

• ✅ Async execution - Start tasks in background, continue working

• ✅ Multi-instance parallelism - Run multiple Claude Code sessions simultaneously

• ✅ Automatic cleanup - No zombie processes

• ✅ Zero config - Works out of the box

• ✅ Cross-platform - Supports Windows, Linux, and macOS

• ✅ CI/CD ready - GitHub Actions workflows included

Related MCP server: mcp-terminal-server

Quick Start

🚀 Install with UVX

Zero configuration - just run:

uvx claudecode-mcp-async-windows

Configure Claude Code

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

{
  "mcpServers": {
    "claude-code-mcp": {
      "command": "uvx",
      "args": ["claudecode-mcp-async-windows"],
      "env": {}
    }
  }
}

Restart Claude Code

Reload or restart Claude Code to load the MCP server.

Usage Examples

🚀 Async Execution (Game Changer!)

Start a long task and continue working immediately:

You:

Please analyze the entire project code and generate a comprehensive technical report

Claude: I'll analyze your entire project and generate a technical report. 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 report is generating, help me write some unit tests

Claude: Sure! Let me write those unit tests for you...

You: (A few minutes later)

Can you check if the report task is finished?

Claude: ✅ Report Complete!

[View Detailed Technical Report]

• Project structure analysis

• Code quality assessment

• Performance optimization recommendations

• Security audit results

⚡ Parallel Execution

Run multiple 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("user@example.com"))  # True
print(validate_email("invalid-email"))    # False

✅ Task Complete!

Why Async?

Problem: Claude Code blocks the parent session while running.

Solution: This MCP server spawns child Claude Code processes that run in the background.

Benefits:

• 🚀 Start a task and continue working immediately

• ⚡ Run multiple tasks in parallel

• 🎯 No blocking, no waiting

• 🧹 Automatic process cleanup

Troubleshooting

Server not showing up?

• Use absolute path in config

• Linux/macOS: Run chmod +x claudecode_mcp_async_server.py

• Restart Claude Code

Task stuck in "running"?

• Wait a moment, large tasks take time

• Check task files:

  • Linux/macOS: ls -la /tmp/claude_code_tasks/

  • Windows: dir %TEMP%\claude_code_tasks\

• View logs:

  • Linux/macOS: tail -f /tmp/claude_code_mcp_debug.log

  • Windows: type %TEMP%\claude_code_mcp_debug.log

Platform-specific notes:

• Windows: Automatic process cleanup (no zombie processes)

• POSIX: Uses SIGCHLD handler for process cleanup

• All platforms: Uses platform-appropriate temp directories

Requirements

• Python 3.6+

• Claude Code CLI installed

Development

Building from Source

Using uv (recommended):

# Install uv if you haven't already
pip install uv

# Build the package
uv build

# Install locally
uv pip install dist/*.whl --system

GitHub Actions

This project includes automated workflows:

1. Test Workflow (.github/workflows/test.yml)

   • Runs on: Windows, Linux, macOS

   • Python versions: 3.8, 3.9, 3.10, 3.11, 3.12

   • Triggered on: push to main/develop/claude branches, pull requests

   • Actions:

     • Build with uv

     • Run import tests

     • Lint with flake8, black, isort

2. Publish Workflow (.github/workflows/publish.yml)

   • Builds distribution packages using uv

   • Publishes to PyPI on release

   • Uploads to GitHub Releases

   • Supports TestPyPI for testing

Publishing to PyPI

Option 1: Automatic (GitHub Release)

1. Create a new release on GitHub

2. Workflow automatically builds and publishes to PyPI

Option 2: Manual (TestPyPI)

1. Go to Actions → Publish to PyPI

2. Run workflow manually

3. Set test_pypi to true for TestPyPI

Setting up PyPI Publishing:

1. Configure trusted publishing in your PyPI project settings

2. Add environment pypi to your GitHub repository

3. No API tokens needed (uses OIDC)

License

MIT License

Questions? Open an issue on GitHub.