docs/MCP_INTEGRATION_COMPLETE.md

MCP Integration: Complete Implementation

Project Status: ✅ COMPLETED

The Model Context Protocol (MCP) integration for the RTS Commander game has been successfully implemented, tested, and documented.

What We've Built

1. Core MCP Server

• File: mcp_server.py
• Port: 8001
• Framework: FastAPI-based MCP server using the mcp Python package
• Integration: Fully integrated with existing game infrastructure

2. Game Interaction Tools

Six powerful tools that allow AI agents to interact with the game:

1. get_game_state() - Retrieve complete game state as JSON
2. get_ai_analysis(language) - Get tactical analysis from built-in AI
3. move_units(unit_ids, target_x, target_y) - Move units to positions
4. attack_unit(attacker_ids, target_id) - Command attacks on enemies
5. build_building(building_type, position_x, position_y, player_id) - Construct structures
6. **send_game_command(command_type, kwargs) - Send generic commands

3. Information Resources

Two resources providing game documentation:

1. game_documentation - Game README documentation
2. game_rules - Game architecture and rules

4. Startup Scripts

Multiple ways to start the servers:

1. start.py - Start main game server (now mentions MCP server)
2. mcp_server.py - Start MCP server independently
3. start_mcp_only.py - Start only the MCP server for testing
4. start_with_mcp.py - Start both servers together

5. Comprehensive Testing

Complete test suite ensuring reliability:

1. test_mcp_server.py - Basic server functionality tests
2. test_mcp_integration.py - Integration tests
3. test_mcp_comprehensive.py - Full setup verification
4. tools/verify_mcp_setup.py - Setup verification script

6. Example Code

Demonstration scripts for developers:

1. examples/mcp_client_example.py - Example client usage
2. examples/mcp_connection_test.py - Connection testing

7. Complete Documentation

Extensive documentation covering all aspects:

1. docs/MCP_INTEGRATION.md - Complete integration guide
2. docs/MCP_IMPLEMENTATION_SUMMARY.md - Technical implementation details
3. docs/MCP_USAGE_GUIDE.md - Usage instructions
4. docs/FINAL_MCP_INTEGRATION_SUMMARY.md - Final project summary
5. examples/README.md - Examples directory documentation
6. tests/README.md - Updated test documentation
7. docs/PROJECT_FILES_INDEX.txt - Updated project file index

Key Features

✅ Full Game Integration

• Uses existing handle_command method for game actions
• Accesses game state through the global manager instance
• Integrates with the existing AI analysis system

✅ Standardized Protocol

• Implements the Model Context Protocol standard
• Compatible with any MCP-compatible AI client
• Well-documented API

✅ Robust Testing

• Unit tests for all components
• Integration tests for complete functionality
• Verification scripts for deployment

✅ Developer-Friendly

• Clear documentation for all components
• Example code for common use cases
• Multiple startup options for different scenarios

Usage Instructions

Quick Start

# Start both servers together
python start_with_mcp.py

# Or start them separately:
# Terminal 1: python start.py
# Terminal 2: python mcp_server.py

Connect an AI Client

Connect any MCP-compatible client to localhost:8001.

Example AI Commands

{
  "tool": "get_game_state"
}

{
  "tool": "move_units",
  "arguments": {
    "unit_ids": ["unit1", "unit2"],
    "target_x": 100,
    "target_y": 200
  }
}

Technical Details

Architecture

• Separate server process for security isolation
• Reuse of existing game infrastructure
• Standardized protocol for maximum compatibility
• Extensible design for future enhancements

Security

• Separate port (8001) from main game server (7860)
• Isolation of AI access from player connections
• Foundation for future authentication and rate limiting

Performance

• Lightweight FastAPI implementation
• Efficient game state access
• Minimal overhead on main game server

Verification

All components have been verified to work correctly:

✅ File structure complete ✅ Requirements properly configured ✅ Imports working ✅ Server creation successful ✅ Tools registered ✅ Resources registered ✅ Example scripts functional ✅ All tests passing

Conclusion

The MCP integration is production-ready and provides a robust foundation for AI agents to interact with the RTS Commander game. The implementation follows best practices for security, performance, and maintainability while providing comprehensive functionality through the standardized Model Context Protocol.