# MCP4DRL - Model Context Protocol for Deep Reinforcement Learning

MCP server that exposes a trained Deep Q-Network (DQN) agent for business process resource allocation through conversational interfaces. Makes "black box" RL systems transparent via natural language queries.

## Features

- **Environment State Queries** - View simulation state, waiting/active cases, resources
- **Q-Value Analysis** - Inspect Q-values for all actions
- **Action Recommendations** - Get agent's top choice with justification
- **Explainability** - Detailed explanations of why actions are chosen
- **Heuristic Comparison** - Compare with FIFO, SPT, EDF, LST baselines
- **Simulation Control** - Step through episodes, reset, run full episodes

## Installation

```bash
pip install -r requirements.txt
```

**Requirements:** Python 3.8+, TensorFlow 2.16+

## Quick Start

### Test locally

```bash
python -m mcp4drl.test_integration
```

### Run MCP server

```bash
# Windows
run_server.bat

# Linux/Mac
chmod +x run_server.sh
./run_server.sh
```

## Claude Desktop Integration

Add to `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "mcp4drl": {
      "command": "cmd.exe",
      "args": ["/c", "C:\\path\\to\\mcp4drl_repo\\run_server.bat"],
      "shell": true
    }
  }
}
```

## Available MCP Tools

| Tool | Description |
|------|-------------|
| `get_environment_state` | Current simulation state |
| `get_eligible_actions` | All possible actions with validity |
| `get_q_values` | Q-values for all actions |
| `get_recommended_action` | Agent's best action |
| `explain_action` | Detailed action explanation |
| `compare_with_heuristic` | Compare with FIFO/SPT/EDF/LST |
| `step_simulation` | Execute one step |
| `reset_simulation` | Reset to initial state |
| `run_episode` | Run full episode with policy |

## Project Structure

```
mcp4drl_repo/
├── mcp4drl/           # Main Python package
│   ├── core/          # Wrappers (simulator, agent)
│   ├── models/        # Pydantic schemas
│   └── tools/         # MCP tool implementations
├── simprocess/        # Business process simulation engine
├── data/              # Model and event log
└── mcp4drl_server.py  # Standalone launcher
```

## Configuration

Environment variables (optional):
- `MCP4DRL_MODEL_PATH` - Path to trained model (.h5)
- `MCP4DRL_EVENT_LOG` - Path to XES event log
- `MCP4DRL_TRANSPORT` - `stdio` (default) or `sse`

## Context

Part of doctoral dissertation on intelligent automation of business process management. Demonstrates that RL systems can be made transparent through conversational interfaces.

## License

Research prototype.