IMPLEMENTATION_SUMMARY.md

# AI Chatbot with MCP Server Implementation Summary

## Overview
This document summarizes the implementation and fixes applied to the AI chatbot with Model Context Protocol (MCP) server for the Todo List App. The implementation includes an OpenAI Agents SDK-based AI assistant that communicates with a custom MCP server to perform task management operations.

## Key Components

### 1. AI Agent (`ai/agents/todo_agent.py`)
- Implements an AI assistant using OpenAI Agents SDK with Google Gemini API
- Connects to MCP server for tool integration
- Handles natural language processing for task management commands
- Fixed ModelSettings configuration issue

### 2. MCP Server (`ai/mcp/server.py`)
- Implements MCP server using python-mcp SDK
- Provides tools for task management: add_task, list_tasks, complete_task, delete_task, update_task
- Handles async database operations through thread-based event loops
- Fixed async context issues with SQLAlchemy

### 3. Task Service (`services/task_service.py`)
- Provides business logic for task management operations
- Handles CRUD operations for tasks with proper authorization
- Uses async SQLAlchemy with SQLModel for database operations

## Issues Fixed

### 1. ModelSettings Configuration Issue
**Problem**: Agent model_settings must be a ModelSettings instance, got dict

**Solution**:
```python
# Before (incorrect)
model_settings={"parallel_tool_calls": False}

# After (correct)
model_settings=ModelSettings(parallel_tool_calls=False)
```

**Files affected**: `ai/agents/todo_agent.py` (line 81)

### 2. User ID Type Conversion Issue
**Problem**: String user IDs were being passed to service methods expecting integers

**Solution**:
```python
# Convert user_id to integer as expected by TaskService
user_id_int = int(user_id) if isinstance(user_id, str) else user_id
```

**Files affected**: `ai/mcp/server.py` (multiple locations)

### 3. Async Context Issues with SQLAlchemy
**Problem**: `greenlet_spawn has not been called; can't call await_only() here`

**Solution**: Implemented thread-based execution with dedicated event loops:
```python
def run_db_operation():
    import asyncio
    # ... imports ...

    async def db_op():
        # Database operations here
        async with AsyncSession(async_engine) as session:
            # ... operations ...
            return result

    # Create new event loop for this thread
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    try:
        return loop.run_until_complete(db_op())
    finally:
        loop.close()

# Run the async operation in a separate thread
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor() as executor:
    future = executor.submit(run_db_operation)
    result = future.result()
```

**Files affected**: `ai/mcp/server.py` (all handler functions)

## Architecture

### Tech Stack
- Python 3.11+
- FastAPI for web framework
- SQLModel for database modeling
- OpenAI Agents SDK with Google Gemini API
- Model Context Protocol (MCP) for tool integration
- SQLAlchemy with asyncpg for PostgreSQL

### Data Flow
1. User sends natural language command to AI agent
2. AI agent processes command and determines appropriate tool to call
3. MCP server receives tool call and executes corresponding handler
4. Handler performs database operations via TaskService
5. Results are returned to AI agent
6. AI agent responds to user in natural language

### Security & Authorization
- User ID validation in all operations
- Tasks are isolated by user ID
- All database operations include proper authorization checks

## Features

### Task Management Operations
1. **Add Task**: Create new tasks with title, description, priority, and due date
2. **List Tasks**: Retrieve all tasks or filter by status (all, pending, completed)
3. **Complete Task**: Mark tasks as completed
4. **Delete Task**: Remove tasks from user's list
5. **Update Task**: Modify task details including title, description, priority, due date, and completion status

### AI Capabilities
- Natural language understanding for task management
- Context-aware responses
- Tool usage for database operations
- Parallel tool call prevention to avoid database locks

## Testing
- Created verification scripts to ensure all fixes are properly implemented
- Syntax checks confirm correct ModelSettings configuration
- Async context handling verified in MCP server
- Thread-based event loops confirmed in place

## Files Modified

1. `ai/agents/todo_agent.py` - Fixed ModelSettings configuration
2. `ai/mcp/server.py` - Fixed async context issues and user ID conversion
3. `requirements.txt` - Added python-mcp dependency

## Conclusion
The AI chatbot with MCP server implementation is now complete with all critical issues fixed. The system can properly handle natural language commands for task management while maintaining proper async context and database operations. The implementation follows best practices for async programming and database access in Python environments.