MCP Deployment and Testing

# MCP Deployment and Testing for OpenAI Apps SDK

## Overview

Deploying and testing MCP servers requires proper HTTPS setup, local development tools, and ChatGPT connector configuration. This skill covers the complete deployment workflow from local testing to production.

## Local Development Setup

### Running the Server

**Python:**
```bash
# Install dependencies
pip install mcp

# Run server
python server.py
# Server running on http://localhost:8000/mcp
```

**TypeScript:**
```bash
# Install dependencies
npm install @modelcontextprotocol/sdk express

# Run server
npx tsx server.ts
# Server running on http://localhost:8000/mcp
```

### Exposing with ngrok

ChatGPT requires HTTPS. Use ngrok for local development:

```bash
# Install ngrok
brew install ngrok  # macOS
# or download from ngrok.com

# Expose local server
ngrok http 8000

# Output:
# Forwarding https://abc123.ngrok.io -> http://localhost:8000
```

Save the HTTPS URL for ChatGPT connector setup.

## MCP Inspector

Test MCP servers without ChatGPT integration:

### Installation

```bash
npx @modelcontextprotocol/inspector@latest
```

### Usage

```bash
# Test local server
npx @modelcontextprotocol/inspector http://localhost:8000/mcp

# Test ngrok URL
npx @modelcontextprotocol/inspector https://abc123.ngrok.io/mcp
```

### Inspector Features

- **List Tools** - View all available tools and schemas
- **Call Tools** - Execute tools with test parameters
- **View Resources** - List and fetch MCP resources
- **Test Widgets** - Preview widget HTML rendering
- **Debug Responses** - Inspect structuredContent and _meta

## ChatGPT Connector Setup

### Enable Developer Mode

1. Open ChatGPT settings
2. Navigate to "Developer settings"
3. Enable "Developer mode"

### Create Connector

1. Go to ChatGPT → Settings → Connectors
2. Click "Add connector"
3. Enter your MCP server URL (HTTPS required)
4. Name your connector
5. Save

### Add to Conversation

1. Start a new conversation
2. Click the connector icon
3. Select your connector
4. Test with natural language prompts

## Testing Workflow

### 1. Local Server Test

```bash
# Start server
python server.py

# Test with curl
curl http://localhost:8000/mcp \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{"method": "tools/list"}'
```

### 2. MCP Inspector Test

```bash
# Run inspector
npx @modelcontextprotocol/inspector http://localhost:8000/mcp

# In inspector:
# - Verify tools list correctly
# - Test each tool with sample inputs
# - Check widget rendering
```

### 3. ngrok Integration Test

```bash
# Expose server
ngrok http 8000

# Test HTTPS endpoint
curl https://abc123.ngrok.io/mcp \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{"method": "tools/list"}'
```

### 4. ChatGPT Integration Test

1. Add connector with ngrok URL
2. Test natural language tool invocation
3. Verify widget rendering
4. Check response accuracy

## Debugging

### Enable Debug Logging

**Python:**
```python
import logging
logging.basicConfig(level=logging.DEBUG)

mcp = FastMCP("server", debug=True)
```

**TypeScript:**
```typescript
const server = new Server(
  { name: "server", version: "1.0.0" },
  { capabilities: { tools: {} }, debug: true }
);
```

### Common Issues

| Issue | Solution |
|-------|----------|
| Connection refused | Check server is running on correct port |
| SSL error | Use ngrok for HTTPS |
| Tool not found | Verify tool registration and naming |
| Widget not rendering | Check `openai/outputTemplate` URI |
| Auth failure | Verify tokens and security config |

### Request Logging

**Python:**
```python
@mcp.middleware
async def log_requests(request, call_next):
    print(f"Request: {request.method} - {request.params}")
    response = await call_next(request)
    print(f"Response: {response}")
    return response
```

## Production Deployment

### Hosting Options

| Platform | Notes |
|----------|-------|
| Railway | Easy Python/Node deployment |
| Render | Free tier available |
| Fly.io | Edge deployment |
| AWS Lambda | Serverless option |
| Google Cloud Run | Container-based |

### Railway Deployment

```bash
# Install Railway CLI
npm install -g @railway/cli

# Login and init
railway login
railway init

# Deploy
railway up
```

### Docker Deployment

**Dockerfile (Python):**
```dockerfile
FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 8000
CMD ["python", "server.py"]
```

**Build and run:**
```bash
docker build -t mcp-server .
docker run -p 8000:8000 mcp-server
```

### Environment Variables

Production configuration:

```bash
# .env.production
PORT=8000
HOST=0.0.0.0
DATABASE_URL=postgresql://...
API_KEY=your_production_key
LOG_LEVEL=INFO
```

### Health Checks

Add a health check endpoint:

**Python:**
```python
@mcp.tool()
def health_check() -> dict:
    """Server health check."""
    return {
        "structuredContent": {
            "status": "healthy",
            "version": "1.0.0",
            "timestamp": datetime.now().isoformat()
        }
    }
```

## Production Checklist

- [ ] HTTPS enabled (required)
- [ ] Environment variables configured
- [ ] Debug mode disabled
- [ ] Logging configured
- [ ] Error handling implemented
- [ ] Rate limiting enabled
- [ ] Health check endpoint
- [ ] Monitoring/alerting setup
- [ ] Database backups (if applicable)
- [ ] CORS configured correctly

## Troubleshooting

### Server Not Responding

```bash
# Check if server is running
curl http://localhost:8000/mcp

# Check port binding
lsof -i :8000

# Check logs
tail -f server.log
```

### Widget Not Loading

1. Check resource registration
2. Verify `mimeType: "text/html+skybridge"`
3. Inspect `_meta.openai/outputTemplate`
4. Test widget HTML in MCP Inspector

### Tool Call Failures

1. Verify tool name matches exactly
2. Check inputSchema validation
3. Review error response format
4. Test with MCP Inspector first

## Additional Resources

### Reference Files

For detailed deployment guides:
- **`references/hosting-guide.md`** - Platform-specific deployment
- **`references/monitoring.md`** - Logging and monitoring setup

### Example Files

Working examples in `examples/`:
- **`examples/Dockerfile`** - Docker deployment example
- **`examples/railway.json`** - Railway configuration

### Official Documentation

- Apps SDK Deployment: https://developers.openai.com/apps-sdk/build/deploy-your-app/
- ChatGPT Connectors: https://help.openai.com/en/articles/12584461-developer-mode-apps-and-full-mcp-connectors-in-chatgpt-beta