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kiranmaya

AI CLI MCP Server

by kiranmaya
README.md
# AI CLI MCP Server

[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![MCP Spec](https://img.shields.io/badge/MCP-2.2.0-green.svg)](https://modelcontextprotocol.io/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Build Status](https://github.com/kiranmaya/ai-cli-mcp/actions/workflows/publish.yml/badge.svg)](https://github.com/kiranmaya/ai-cli-mcp/actions)

A production-ready [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that acts as a secure, unified gateway to both **OpenAI Codex CLI** and **Google Antigravity CLI (`agy`)**.

It empowers primary AI orchestrators (such as **Claude Desktop**, **Gemini**, **Cursor**, or **Windsurf**) to delegate complex coding tasks, file refactorings, and deep automated code reviews to autonomous CLI agents running in **YOLO mode** with robust process lifecycle supervision.

---

## Architecture

```
                 ┌───────────────────────────────────┐
                 │          Any AI Agent             │
                 │ Claude / Gemini / Cursor / etc.   │
                 └─────────────────┬─────────────────┘
                                   │ MCP Protocol
                                   ▼
                ┌────────────────────────────────────┐
                │        Python MCP Server           │
                │        (ai_cli_mcp_server)         │
                ├────────────────────────────────────┤
                │ • codex_run     • codex_review     │
                │ • antigravity_run • antigravity_rvw│
                │ • cli_status    • get_agent_skills │
                └──────────────┬──────────────┬──────┘
                               │              │
                   ┌───────────┘              └───────────┐
                   ▼                                      ▼
     ┌───────────────────────────┐          ┌───────────────────────────┐
     │      OpenAI Codex CLI     │          │  Google Antigravity CLI   │
     │  (--dangerously-bypass)   │          │  (--dangerously-skip)     │
     └───────────────────────────┘          └───────────────────────────┘
```

---

## Key Features

- **Unified CLI Gateway**: Controls both OpenAI Codex and Google Antigravity agents through clean, standard MCP tools.
- **True Autonomous YOLO Mode**:
  - Automatically applies `--dangerously-bypass-approvals-and-sandbox` for Codex.
  - Automatically applies `--dangerously-skip-permissions` for Antigravity (`agy`).
  - Guarantees non-blocking, headless execution without hanging on confirmation dialogs.
- **Safer by Design**: Does **not** expose arbitrary shell execution (`cmd.exe`/`bash`). Only structured, sandboxed tasks are dispatched to vetted agent CLIs.
- **Process Supervision & Windows Tree-Killing**: When a task times out, child processes (compilers, servers, node) are terminated cleanly via process-tree signals.
- **Built-in Agent Skills**: Exposes an embedded, self-contained skills document via tool (`get_agent_skills`) and MCP resource (`skills://usage-guide`) so calling agents know how to orchestrate multi-agent workflows.
- **Multi-Client Support**: Out-of-the-box configuration for Claude Desktop, Cursor, Antigravity, Gemini, and VS Code.

---

## Available MCP Tools

| Tool | Parameters | Description |
| :--- | :--- | :--- |
| `codex_run` | `prompt`, `working_directory`, `model?`, `timeout?`, `yolo?` | Runs an autonomous coding task with OpenAI Codex CLI. |
| `antigravity_run` | `prompt`, `working_directory`, `model?`, `timeout?`, `yolo?` | Runs an autonomous coding task with Google Antigravity CLI. |
| `codex_review` | `working_directory`, `instructions?`, `model?`, `timeout?`, `uncommitted?` | Runs a non-interactive Git diff code review via Codex. |
| `antigravity_review` | `working_directory`, `instructions?`, `model?`, `timeout?` | Runs an automated codebase critique and review via Antigravity. |
| `cli_status` | *(none)* | Inspects local CLI binary health, versions, paths, and platform info. |
| `get_agent_skills` | *(none)* | Returns the comprehensive orchestration guide for calling agents. |

---

## Quickstart

### 1. Installation

```bash
# Clone the repository
git clone https://github.com/kiranmaya/ai-cli-mcp.git
cd ai-cli-mcp

# Install dependencies or install in editable mode
pip install -e .
```

### 2. Verify Host Binaries

Run the server status check directly in Python:
```bash
python -c "import asyncio, server; print(asyncio.run(server.cli_status()))"
```

### 3. Add to Claude Desktop

Edit `%APPDATA%\Claude\claude_desktop_config.json`:
```json
{
  "mcpServers": {
    "ai-cli-gateway": {
      "command": "python",
      "args": [
        "C:/Projects2026/AgentsCLI_MCP_Server/ai_cli_mcp_server.py"
      ],
      "env": {
        "CLI_YOLO_MODE": "true"
      }
    }
  }
}
```

### 4. Add to Cursor IDE

In Cursor, add to `.cursor/mcp.json`:
```json
{
  "mcpServers": {
    "ai-cli-gateway": {
      "command": "python",
      "args": [
        "C:/Projects2026/AgentsCLI_MCP_Server/ai_cli_mcp_server.py"
      ]
    }
  }
}
```

*(For detailed setup in Antigravity IDE, Windsurf, and VS Code Cline, see [INSTALLATION_AND_CLIENTS.md](INSTALLATION_AND_CLIENTS.md).)*

---

## Multi-Agent Workflow Example

A calling agent (e.g. Claude) can execute an end-to-end task and peer review:

```python
# 1. Dispatch feature implementation to Codex
codex_run(
    prompt="Implement JWT refresh token rotation with SQLite in src/auth.py",
    working_directory="C:/MyProject"
)

# 2. Dispatch cross-verification review to Antigravity
antigravity_review(
    working_directory="C:/MyProject",
    instructions="Audit security edge cases for token invalidation in src/auth.py"
)
```

---

## Project Structure

```
ai-cli-mcp/
├── ai_cli_mcp_server.py     # Main CLI entrypoint
├── server.py                # MCP Server & Tool definitions
├── config.py                # Path discovery & sandbox validation
├── process.py               # Async process execution & tree killing
├── cli/
│   ├── codex.py             # OpenAI Codex CLI adapter
│   └── antigravity.py       # Google Antigravity CLI adapter
├── models/
│   └── requests.py          # Pydantic schemas & response models
├── skills/
│   └── document.md          # In-depth agent usage & skills documentation
├── pyproject.toml           # Packaging & build configuration
├── requirements.txt         # Core dependencies
├── INSTALLATION_AND_CLIENTS.md # Client configuration reference
└── README.md
```

---

## Environment Configuration

| Variable | Default | Purpose |
| :--- | :--- | :--- |
| `CLI_YOLO_MODE` | `true` | Runs commands with permission-bypass flags. |
| `CLI_DEFAULT_TIMEOUT` | `300` | Default timeout in seconds (5 min). |
| `CLI_MAX_TIMEOUT` | `1800` | Maximum timeout ceiling (30 min). |
| `ALLOWED_WORKING_DIRECTORIES` | `*` | Sandbox directory whitelist (comma-separated). |
| `CODEX_CLI_PATH` | Auto | Override path to `codex.exe`. |
| `AGY_CLI_PATH` | Auto | Override path to `agy.exe`. |

---

## License

MIT License. See [LICENSE](LICENSE) for details.

TDQS

A3.9/5.0

Scored across 6 tools

Disambiguation4/5

The two run tools and two review tools are intentionally parallel but clearly distinguished by vendor (Codex vs. Antigravity), and cli_status/get_agent_skills are wholly distinct. The main confusion risk is between codex_run and antigravity_run, but descriptions make the target CLI explicit.

Naming Consistency4/5

The core tools follow a consistent vendor_action pattern (codex_run, antigravity_run, codex_review, antigravity_review). cli_status and get_agent_skills break the pattern slightly, but they are also different kinds of operations and the names remain readable and predictable.

Tool Count5/5

Six tools is a well-scoped set for a server that wraps two external CLI agents. Each tool covers a distinct operational need: execute, review, status check, and usage guidance.

Completeness4/5

The server covers the primary workflows one would expect: running and reviewing with both Codex and Antigravity, plus environment status and skill guidance. Minor gaps like model listing or installation tooling exist, but agents can work around them.

Maintenance

ActivityNo data
ResponsivenessNo issues