# MCP Sentiment Analysis - Solution

## Problem Summary

Your original `working_mcp_test.py` script was hanging because of an issue with the MCP (Model Context Protocol) connection process. Specifically:

1. ✅ **STDIO client connection** - This worked fine (0.0s)
2. ✅ **ClientSession creation** - This also worked fine  
3. ❌ **Session initialization** - This was timing out after 30-45 seconds

The issue was in the `session.initialize()` step, which handles the MCP protocol handshake between your client and the remote server.

## Root Cause

The problem was using the **wrong MCP client**:
- ❌ **Low-level `mcp.ClientSession`** - requires manual protocol handling and times out
- ✅ **High-level `smolagents.mcp_client.MCPClient`** - handles all protocol complexity automatically

Based on the [Hugging Face MCP Course](https://huggingface.co/learn/mcp-course/unit2/gradio-client), the `smolagents` library provides the proper high-level interface for MCP connections.

## ✅ PRIMARY SOLUTION: smolagents MCPClient

**This is the best solution** - fast, reliable, and uses proper MCP protocol.

### Installation
```bash
pdm add "smolagents[mcp]"
```

### Code: `usage/sentiment_mcp.py`

```python
#!/usr/bin/env python3
"""
MCP Sentiment Analysis using smolagents MCPClient.

To run this script:
    pdm run python usage/sentiment_mcp.py
"""

import time
from smolagents.mcp_client import MCPClient

def analyze_sentiment_mcp(text):
    """Analyze sentiment using MCP protocol."""
    mcp_client = None
    try:
        mcp_client = MCPClient(
            {"url": "https://freemansel-mcp-sentiment.hf.space/gradio_api/mcp/sse"}
        )
        
        tools = mcp_client.get_tools()
        sentiment_tool = tools[0]  # The sentiment analysis tool
        
        result = sentiment_tool(text=text)
        return result
        
    finally:
        if mcp_client:
            mcp_client.disconnect()

def main():
    test_texts = [
        "I love this product! It's amazing!",
        "This is terrible. I hate it.",
        "It's okay, nothing special.",
    ]

    for i, text in enumerate(test_texts, 1):
        print(f"Test {i}: '{text}'")
        
        start_time = time.time()
        result = analyze_sentiment_mcp(text)
        elapsed = time.time() - start_time
        
        print(f"  📊 Polarity: {result['polarity']}")
        print(f"  📊 Subjectivity: {result['subjectivity']}")
        print(f"  📊 Assessment: {result['assessment']}")
        print(f"  ⏱️  Time: {elapsed:.2f}s")
        print()

if __name__ == "__main__":
    main()
```

### Results with smolagents
```
Test 1: 'I love this product! It's amazing!'
  📊 Polarity: 0.69
  📊 Subjectivity: 0.75
  📊 Assessment: positive
  ⏱️  Time: 0.11s

Test 2: 'This is terrible. I hate it.'
  📊 Polarity: -0.9
  📊 Subjectivity: 0.95
  📊 Assessment: negative
  ⏱️  Time: 0.11s
```

**Performance:** ~0.11 seconds per request! 🚀

## 🔄 BACKUP SOLUTION: Gradio Client

If you prefer not to use MCP protocol, the Gradio client approach also works reliably.

### File: `usage/sentiment_gradio.py`

```python
#!/usr/bin/env python3
"""
Gradio Sentiment Analysis (Backup Solution).

To run this script:
    pdm run python usage/sentiment_gradio.py
"""

import time
from gradio_client import Client

def analyze_sentiment_gradio(text):
    """Analyze sentiment using Gradio client."""
    client = Client("https://freemansel-mcp-sentiment.hf.space")
    result = client.predict(text, api_name="/predict")
    return result

def main():
    test_texts = [
        "I love this product! It's amazing!",
        "This is terrible. I hate it.",
        "It's okay, nothing special.",
    ]

    client = Client("https://freemansel-mcp-sentiment.hf.space")
    
    for i, text in enumerate(test_texts, 1):
        print(f"Test {i}: '{text}'")
        
        start_time = time.time()
        result = client.predict(text, api_name="/predict")
        elapsed = time.time() - start_time
        
        print(f"  📊 Polarity: {result.get('polarity', 'N/A')}")
        print(f"  📊 Subjectivity: {result.get('subjectivity', 'N/A')}")
        print(f"  📊 Assessment: {result.get('assessment', 'N/A')}")
        print(f"  ⏱️  Time: {elapsed:.2f}s")
        print()

if __name__ == "__main__":
    main()
```

**Performance:** ~1.3 seconds per request.

## Comparison

| Method | Setup | Speed | Protocol | Recommended |
|--------|-------|-------|----------|-------------|
| **smolagents MCP** | `pdm add "smolagents[mcp]"` | **0.11s** | ✅ Native MCP | ⭐ **Best** |
| Gradio Client | `gradio_client` (already installed) | 1.3s | Direct API | ✅ Good backup |
| Low-level MCP | ❌ | ❌ Timeout | ❌ Broken | ❌ Don't use |

## Running the Solutions

### Primary (smolagents):
```bash
cd /c/Users/phil7/Code/mcp-sentiment
pdm add "smolagents[mcp]"  # If not already installed
pdm run python usage/sentiment_mcp.py
```

### Backup (Gradio):
```bash
cd /c/Users/phil7/Code/mcp-sentiment
pdm run python usage/sentiment_gradio.py
```

### Debugging:
```bash
# If you have import issues
pdm run python usage/debug_imports.py
```

## Key Learnings

1. **Use High-Level Clients**: Always prefer `smolagents.MCPClient` over low-level `mcp.ClientSession`
2. **Follow Official Docs**: The [Hugging Face MCP Course](https://huggingface.co/learn/mcp-course/unit2/gradio-client) provides the correct approach
3. **MCP Works Great**: When used properly, MCP is actually faster than direct API calls!
4. **Protocol Abstraction Matters**: High-level libraries handle complex protocol details

## Conclusion

The **smolagents MCPClient** is the optimal solution, providing:
- ✅ **Fastest performance** (0.11s vs 1.3s)
- ✅ **Proper MCP protocol usage**
- ✅ **No connection issues**
- ✅ **Clean, maintainable code**

The original issue was simply using the wrong level of MCP client. The Hugging Face documentation showed us the right way! 🎉