Spaces:

Luigi
/

rts-commander

Sleeping

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

Available Resources

The MCP server provides the following resources:

game_documentation - Provides the game's README documentation
game_rules - Provides the game's architecture and rules documentation

How to Use the MCP Server

1. Start the Servers

First, you need to start both the main game server and the MCP server:

# Terminal 1: Start main game server
cd /home/luigi/rts/web
python start.py

# Terminal 2: Start MCP server
cd /home/luigi/rts/web
python mcp_server.py

Or you can start both with a single command:

cd /home/luigi/rts/web
python start_with_mcp.py

2. Verify the Server is Running

Check that the MCP server is listening on port 8001:

netstat -tlnp | grep :8001

You should see output showing that a process is listening on port 8001.

3. Connect an MCP-Compatible Client

To actually use the MCP server, you need an MCP-compatible client. Some options include:

Option 1: Use a Generic MCP Client

You can create a simple Python client to test the connection:

# mcp_test_client.py
import asyncio
import json
from mcp.client.stdio import stdio_client
from mcp.types import InitializeRequest

async def test_mcp_client():
    # This is a simplified example
    print("Connecting to MCP server...")
    # In practice, you would connect to localhost:8001
    
if __name__ == "__main__":
    asyncio.run(test_mcp_client())

Option 2: Use an AI Framework with MCP Support

Some AI frameworks have built-in MCP support. You would typically configure them to connect to:

Host: localhost
Port: 8001

Option 3: Manual Testing with curl

You can test basic connectivity with curl:

curl -v http://localhost:8001

Note that MCP typically uses a more complex protocol than simple HTTP requests, so full functionality requires an actual MCP client.

Common Issues and Troubleshooting

"Connection refused" Error

If you get a connection refused error:

Make sure both servers are running
Check that the MCP server is listening on port 8001:
```
netstat -tlnp | grep :8001
```
Verify firewall settings if running on a remote machine

"claude: command not found"

The claude command is not a standard command. It would only exist if you had installed a specific AI client that provides this command. Our implementation provides the MCP server, but you need to separately obtain an MCP client.

Port Conflicts

If port 8001 is already in use:

You can modify the port in mcp_server.py
Or stop the process using port 8001:
```
lsof -i :8001
kill -9 <PID>
```

Example Usage Scenarios

Scenario 1: Getting Game State

An AI agent could call the get_game_state() tool to understand the current situation:

{
  "tool": "get_game_state"
}

Scenario 2: Moving Units

An AI agent could move units to a position:

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

Scenario 3: Building a Structure

An AI agent could build a barracks:

{
  "tool": "build_building",
  "arguments": {
    "building_type": "barracks",
    "position_x": 150,
    "position_y": 150,
    "player_id": 0
  }
}

Security Considerations

The MCP server runs on a separate port (8001) from the main game server (7860) to isolate AI access from player connections. In a production environment, you should consider:

Adding authentication for MCP clients
Implementing rate limiting for commands
Adding input validation for all commands
Running the server in a secure network environment

Extending the Integration

To add new tools or resources:

Add new methods to the _register_tools or _register_resources methods in RTSGameMCP in mcp_server.py
Implement the functionality using existing game infrastructure
Test the new functionality

Conclusion

The MCP integration provides a robust foundation for AI agents to interact with the RTS Commander game. While you need an MCP-compatible client to actually use it, the server is fully functional and ready for integration with AI systems.