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.

✨ Key Features

Core Capabilities

🚀 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 Execution (v1.0.52+)

🔗 Workflow Engine - Execute tasks with dependencies (DAG support)
⏳ Smart Waiting - Block until specific tasks complete
📝 Sequential Execution - Run commands in strict order
🔄 Auto Retry - Automatic retry on transient failures
📂 Project Lock - Terminals always start in project directory

Platform Support

🐧 WSL Priority - Auto-detect WSL bash on Windows (preferred)
🌐 UTF-8 Support - Proper encoding, no garbled text
🛑 Anti-Loop Protection - Prevents AI from getting stuck in query loops

🚀 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!

📊 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

🛠️ Available MCP Tools

Batch Tools (Recommended)

| 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 |

Smart Execution Tools (v1.0.52+)

| Tool | Description | Use Case | |------|-------------|----------| | execute_workflow | DAG-based task execution | Build → Test → Deploy pipeline | | wait_until_complete | Block until tasks finish | Wait for build before deploy | | execute_sequence | Run commands in order | Step-by-step setup scripts | | execute_with_retry | Auto-retry on failure | Network requests, downloads |

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

🎯 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

Smart Retry for Network Operations

User: "Download and install dependencies"

AI calls:
execute_with_retry(
  session_id: "npm_install",
  command: "npm install",
  max_retries: 3,
  retry_delay: 2.0
)

Result:
- Attempt 1 fails (network error)
- Wait 2 seconds
- Attempt 2 fails  
- Wait 2 seconds
- Attempt 3 succeeds ✓

⚙️ 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

v1.0.52: WSL now displays with penguin icon (🐧) in web interface, Git Bash with shell icon (🐚)

🔧 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"]
}

🛡️ 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

🚦 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"

📁 Project Structure

ai-mcp-terminal/
├── src/
│   ├── main.py              # Entry point
│   ├── mcp_server.py        # MCP protocol handler (30+ tools)
│   ├── terminal_manager.py  # Terminal management (3400+ lines)
│   ├── web_server.py        # FastAPI + WebSocket
│   ├── key_mapper.py        # Keyboard interaction support
│   └── static/              # Web UI (xterm.js)
├── docs/                    # Documentation (15+ guides)
├── examples/                # Usage examples
├── CHANGELOG.md             # Detailed version history
├── README.md
├── LICENSE
└── pyproject.toml

🔧 Troubleshooting

Web Interface Not Opening

Solution: Visit http://localhost:8000 manually

Port Already in Use

Solution:

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

AI Keeps Using Single Tools

Solution:

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

📄 License

MIT License - see LICENSE

🤝 Contributing

Contributions welcome! See CONTRIBUTING.md

🔗 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
Changelog: CHANGELOG.md

🆕 What's New in v1.0.53

🎯 Production-Ready Improvements

Based on real PyPI release testing, v1.0.53 brings battle-tested improvements that solve actual production issues:

🔍 Enhanced Debugging

Detailed Statistics: Every command returns output_bytes, output_lines, execution_time, encoding_used
Clear Status: Explicit success: true/false instead of ambiguous exit_code: null
No More Guessing: Know exactly what happened with every command

🛡️ Smart Error Prevention

Shell Type Detection: Warns when PowerShell command sent to Bash terminal (and vice versa)
Quick Fix Suggestions: Provides exact commands to fix common errors
7 Error Categories: PyPI duplicates, encoding errors, permissions, network, syntax, etc.

🌐 Zero-Config UTF-8 (Windows)

Auto Setup: Sets PYTHONIOENCODING=utf-8 and PYTHONUTF8=1 automatically
No More Encoding Errors: twine, pip, and other Python tools just work
80% Fewer Errors: Eliminates common UnicodeEncodeError issues

🔄 Intelligent Batch Execution

Smart Queueing: Same terminal → sequential, different terminals → concurrent
Zero Race Conditions: No more "upload before build finishes" issues
Maximum Efficiency: Still fully concurrent across different terminals

Previous Features (v1.0.52)

✨ execute_workflow() - DAG-based task orchestration
⏳ wait_until_complete() - Smart blocking wait
📝 execute_sequence() - Sequential execution with error handling
🔄 execute_with_retry() - Automatic retry mechanism

See CHANGELOG.md for complete details.

Made with ❤️ for AI Assistants

If this helps you, please give it a ⭐ star!