docs/MCP_IMPLEMENTATION_SUMMARY.md

# MCP Implementation Summary

## Overview

This document summarizes the implementation of Model Context Protocol (MCP) support for the RTS Commander game.

## Features Implemented

### 1. MCP Server (`mcp_server.py`)
- Created a FastMCP server that runs on port 8001
- Integrated with the existing game infrastructure
- Exposes game state and actions through the Model Context Protocol

### 2. Tools
Implemented the following tools for AI agents to interact with the game:

1. **get_game_state()** - Returns the current game state as JSON
2. **get_ai_analysis(language)** - Returns AI tactical analysis in the specified language
3. **move_units(unit_ids, target_x, target_y)** - Moves units to a target position
4. **attack_unit(attacker_ids, target_id)** - Commands units to attack an enemy unit
5. **build_building(building_type, position_x, position_y, player_id)** - Builds a structure
6. **send_game_command(command_type, **kwargs)** - Sends a generic command to the game

### 3. Resources
Implemented the following resources for AI agents to access game information:

1. **game_documentation** - Provides the game's README documentation
2. **game_rules** - Provides the game's architecture and rules documentation

### 4. Integration
- Integrated with the existing `handle_command` method in the ConnectionManager
- Uses the existing AI analysis system for tactical advice
- Accesses game state through the global `manager` instance

## Testing

### Unit Tests
- Created `test_mcp_server.py` for basic server functionality testing
- Created `test_mcp_integration.py` for integration testing

### Test Coverage
- Server creation and configuration
- Tool registration
- Resource registration

## Documentation

### New Files
- `docs/MCP_INTEGRATION.md` - Complete integration guide
- `docs/MCP_IMPLEMENTATION_SUMMARY.md` - This document
- `examples/mcp_client_example.py` - Example client usage
- `examples/README.md` - Examples directory documentation

### Updated Files
- `README.md` - Added MCP integration section
- `docs/README.md` - Added MCP integration to documentation index
- `docs/PROJECT_FILES_INDEX.txt` - Added MCP files to project index
- `tests/README.md` - Added MCP tests to test documentation
- `requirements.txt` - Added MCP package dependency

## Usage

### Starting the Servers
To start both the main game server and the MCP server:

```bash
python start_with_mcp.py
```

Or start them separately:

```bash
# Terminal 1: Start main game server
python start.py

# Terminal 2: Start MCP server
python mcp_server.py
```

### Connecting an AI Client
AI clients can connect to the MCP server at `localhost:8001`.

For example, with Claude:
```bash
claude --mcp-server localhost:8001
```

## Implementation Details

### Architecture
The MCP server is implemented as a separate FastAPI application that runs alongside the main game server. It communicates with the game through the existing ConnectionManager instance.

### Security
The MCP server runs on a separate port (8001) from the main game server (7860) to isolate AI access from player connections.

### Extensibility
The implementation is designed to be easily extensible:
- New tools can be added by implementing new methods in the `_register_tools` method
- New resources can be added by implementing new methods in the `_register_resources` method

## Future Improvements

1. **Authentication** - Add authentication for MCP clients
2. **Rate Limiting** - Implement rate limiting for commands
3. **Input Validation** - Add more comprehensive input validation
4. **Advanced Tools** - Add more sophisticated tools for complex game interactions
5. **Real-time Updates** - Implement real-time game state updates through MCP

## Conclusion

The MCP integration provides a robust foundation for AI agents to interact with the RTS Commander game. It exposes all necessary game information and actions through a standardized protocol, making it easy for AI agents to understand and control the game.