ai-mcp-terminal 1.0.51

AI终端管理器 - 支持MCP协议的多线程终端系统，提供并发执行、智能调度、交互检测、Web界面等功能

AI-MCP Terminal

🚀 Multi-threaded terminal management for AI assistants with real-time web monitoring

Solve terminal blocking issues - Commands run async, never block AI operations. Monitor up to 100 concurrent terminals with intelligent cleanup and system tracking.

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✨ Key Features

• 🚀 Async Execution - Commands never block AI operations
• 🔢 Multi-Threading - 100 concurrent terminals with ThreadPoolExecutor
• 🧹 Auto Cleanup - Smart idle session detection & memory management
• ⚡ Batch Operations - Execute across multiple terminals simultaneously
• 📊 Web Monitor - Real-time xterm.js interface with system stats
• 🐧 Smart Shell - Auto-detect best shell (bash > PowerShell > cmd)
• 🌐 UTF-8 Support - Proper encoding, no garbled text
• 🛑 Anti-Loop Protection - Prevents AI from getting stuck in query loops

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🚀 Quick Start (1 Minute)

Step 1: Add MCP Configuration

Add to your Cursor/Cline MCP settings:

{
  "mcpServers": {
    "ai-mcp-terminal": {
      "command": "uvx",
      "args": ["ai-mcp-terminal"],
      "env": {}
    }
  }
}

Step 2: Restart IDE

Step 3: Start Using

In Cursor:

Create 3 terminals and run system checks in parallel

AI will use create_batch for true concurrency!

Browser auto-opens → http://localhost:8000 → View all terminals in real-time!

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📊 Web Interface

Auto-opens at http://localhost:8000

Features:

• 📺 Real-time xterm.js terminals
• 📊 CPU/Memory/System stats
• 🔄 Live output streaming
• 🎯 Click to expand terminals
• 🛑 Shutdown server button

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🛠️ Available MCP Tools

Core Tools

Tool	Description	Concurrency
create_batch	Create multiple terminals + execute	✅ 100 threads
execute_batch	Execute across terminals	✅ 100 threads
get_batch_output	Get all outputs	✅ 100 threads
check_completion	Check status	✅ 100 threads
broadcast_command	Send to all terminals	✅ Async

Single Tools (Use batch tools instead!)

Tool	Use Instead
create_session	→ create_batch
execute_command	→ execute_batch
get_output	→ get_batch_output

Why batch tools?

• 10x faster (parallel execution)
• 1 call instead of 10 calls
• Non-blocking design

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🎯 Use Cases

Multi-Service Development

User: "Start frontend, backend, and database"

AI calls:
create_batch(sessions=[
  {name: "frontend", cwd: "./web", initial_command: "npm run dev"},
  {name: "backend", cwd: "./api", initial_command: "python app.py"},
  {name: "db", cwd: "./", initial_command: "docker-compose up"}
])

Result: 3 services start simultaneously, web interface shows all

System Information Gathering

User: "Check system info"

AI calls:
create_batch(sessions=[
  {name: "cpu", cwd: ".", initial_command: "wmic cpu get name"},
  {name: "mem", cwd: ".", initial_command: "wmic memorychip get capacity"},
  {name: "disk", cwd: ".", initial_command: "wmic logicaldisk get size,freespace"},
  {name: "os", cwd: ".", initial_command: "systeminfo"}
])

Later:
get_batch_output(session_ids=["cpu", "mem", "disk", "os"])

Result: All info gathered in parallel, 4x faster than serial

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⚙️ Configuration

Optional environment variables:

{
  "mcpServers": {
    "ai-mcp-terminal": {
      "command": "uvx",
      "args": ["ai-mcp-terminal"],
      "env": {
        "AI_MCP_PREFERRED_SHELL": "bash"
      }
    }
  }
}

Shell Priority:

• Windows: WSL bash → Git Bash → powershell → cmd
• macOS: zsh → bash → sh
• Linux: bash → zsh → sh

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🔧 Installation Options

Option 1: UVX (Recommended)

```json
{

"command": "uvx", "args": ["ai-mcp-terminal"] }

**No installation needed!** UV handles everything.

### Option 2: PIPX

```bash
pipx install ai-mcp-terminal

```json
{

"command": "ai-mcp-terminal" }

### Option 3: PIP

```bash
pip install ai-mcp-terminal

{
      "command": "python",
  "args": ["-m", "src.main"]
}

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🛡️ Anti-Loop Protection

Problem: AI gets stuck querying terminal repeatedly

Solution: Built-in query counter

• Query 1-2: Normal
• Query 3-4: ⚠️ Warning + stop instruction
• Query ≥5: 🔪 Auto-terminate process

Result: AI never loops, always proceeds with tasks

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🚦 How AI Should Use This

✅ Correct Pattern

Dialog 1:
User: "Deploy React app"
AI: 
  1. create_batch(...) 
  2. Reply: "Deploying in background..."
  3. END conversation

Dialog 2 (later):
User: "Is it done?"
AI:
  1. check_completion(...)
  2. Reply: "Still running..." or "Done!"
  3. END conversation

❌ Wrong Pattern (Fixed by protection)

Dialog 1:
User: "Deploy React app"
AI:
  1. execute_command(...)
  2. get_output(...) → running
  3. get_output(...) → running  [Query 2]
  4. get_output(...) → running  [Query 3 - WARNING]
  5. get_output(...) → running  [Query 4]
  6. get_output(...) → AUTO-KILLED [Query 5]
  7. Error: "Loop detected, process terminated"

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📁 Project Structure

ai-mcp-terminal/
├── src/
│   ├── main.py              # Entry point
│   ├── mcp_server.py        # MCP protocol handler
│   ├── terminal_manager.py  # Terminal management (2600+ lines)
│   ├── web_server.py        # FastAPI + WebSocket
│   └── static/              # Web UI (xterm.js)
├── docs/                    # Documentation
├── examples/                # Usage examples
├── README.md
├── LICENSE
└── pyproject.toml

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🔧 Troubleshooting

Web Interface Not Opening

Solution: Visit http://localhost:8000 manually

Port Already in Use

Solution:

1. Auto-finds next available port
2. Or click shutdown in existing interface

AI Keeps Using Single Tools

Solution:

1. Restart IDE (MCP caches tool definitions)
2. Check tool descriptions loaded correctly

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📄 License

MIT License - see LICENSE

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🤝 Contributing

Contributions welcome! See CONTRIBUTING.md

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🔗 Links

• PyPI: https://pypi.org/project/ai-mcp-terminal/
• GitHub: https://github.com/kanniganfan/ai-mcp-terminal
• Issues: https://github.com/kanniganfan/ai-mcp-terminal/issues

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Made with ❤️ for AI Assistants

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