# Feedback Integration with MCP Tools ✅
## What Was Added
You can now provide feedback directly from **Claude Desktop** or **Cursor** using MCP tools!
## New MCP Tools
### 1. `submit_feedback`
Rate a tool execution to improve RL learning.
**Parameters**:
- `execution_id` (required): The ID from the tool execution result
- `rating` (required): 1-5 stars (5 = excellent, 1 = poor)
- `feedback` (optional): Text comment about the execution
**Example**:
```
After executing get_dimensions, you get:
{
"status": "success",
"data": {...},
"execution_id": 567 ← Automatically included!
}
Then call submit_feedback:
{
"execution_id": 567,
"rating": 5,
"feedback": "Perfect results!"
}
```
### 2. `get_recent_executions`
Find recent tool executions that can be rated.
**Parameters**:
- `tool_name` (optional): Filter by specific tool
- `limit` (optional): Number of results (default: 10)
## How It Works
1. **Every tool execution now includes `execution_id`** in the result
2. **You can immediately rate it** using `submit_feedback`
3. **RL system learns** from your feedback in real-time
## Example Workflow in Claude/Cursor
```
User: "Get all dimensions for the application"
Claude: [Executes get_dimensions]
Result: {
"status": "success",
"data": [...],
"execution_id": 568
}
User: "That worked great! Rate it 5 stars"
Claude: [Calls submit_feedback]
{
"execution_id": 568,
"rating": 5,
"feedback": "Perfect results!"
}
Result: {
"status": "success",
"message": "Feedback submitted: 5 stars"
}
```
## Benefits
✅ **No need to remember execution IDs** - they're in every result
✅ **Immediate feedback** - rate right after execution
✅ **Seamless integration** - works in Claude Desktop and Cursor
✅ **Real-time learning** - RL system improves immediately
## Files Changed
- ✅ `fccs_agent/tools/feedback.py` - New feedback tools
- ✅ `fccs_agent/agent.py` - Integrated feedback tools, added execution_id to results
- ✅ `FEEDBACK_GUIDE.md` - Updated with MCP tool instructions
## Testing
After restarting your MCP server, you should see:
- `submit_feedback` in the tool list
- `get_recent_executions` in the tool list
- `execution_id` in all tool execution results
## Next Steps
1. **Restart your MCP server** to load the new tools
2. **Execute any tool** - notice the `execution_id` in the result
3. **Rate it** using `submit_feedback` with the execution_id
4. **Watch RL confidence improve** - check with `python check_rl_status.py`
🎯 **Goal**: Use `submit_feedback` after successful tool executions to boost RL confidence from 64% to 75%+!
