# ✅ mcp-perturbation Deployment SUCCESS

**Deployment Date**: 2026-01-20/21
**Status**: ✅ **DEPLOYED AND RUNNING**
**Service URL**: https://mcp-perturbation-ondu7mwjpa-uc.a.run.app

---

## 🎉 Deployment Summary

After 6 deployment attempts, **mcp-perturbation is now successfully running on Google Cloud Run!**

### Final Configuration
- **Region**: us-central1
- **Memory**: 4Gi
- **CPU**: 2 cores
- **Transport**: SSE (Server-Sent Events)
- **Port**: 8080
- **Python**: 3.11
- **Framework**: FastMCP 0.2.0+
- **Model**: GEARS (Graph-Enhanced Gene Activation and Repression Simulator)

---

## 🐛 Issues Fixed

### Attempt 1-3: Build Configuration Errors
**Issue**: Multiple build-backend and dependency errors
**Fix**: Corrected pyproject.toml configuration

### Attempt 4-5: FastMCP Version Mismatch
**Issue**: `TypeError: FastMCP.run() got an unexpected keyword argument 'port'`
**Root Cause**: Using `fastmcp>=0.1.0` which doesn't support port/host arguments
**Fix**: Updated to `fastmcp>=0.2.0`

### Attempt 6: Import Path Error ✅ FINAL FIX
**Issue**: Still getting TypeError even with correct version
**Root Cause**: Using old import path `from mcp.server.fastmcp import FastMCP`
**Fix**: Changed to modern import `from fastmcp import FastMCP`
**Result**: **DEPLOYMENT SUCCESSFUL!**

---

## 📊 Deployment Verification

### Cloud Run Logs Confirm Success:

```
✅ Application startup complete
✅ Uvicorn running on http://0.0.0.0:8080
✅ Starting MCP server 'perturbation' with transport 'sse' on http://0.0.0.0:8080/sse
✅ Default STARTUP TCP probe succeeded after 1 attempt
✅ Service serving 100 percent of traffic
```

### HTTP Responses:
- SSE endpoint (`/sse`): **200 OK** ✅
- Server is actively processing requests ✅

---

## 🧪 Testing the Deployment

### Option 1: Use with Claude Desktop (Recommended)

Add to `~/Library/Application Support/Claude/claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "perturbation": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/client-sse",
        "https://mcp-perturbation-ondu7mwjpa-uc.a.run.app/sse"
      ]
    }
  }
}
```

Then restart Claude Desktop.

### Option 2: Test with MCP Client Library

```python
from mcp import ClientSession
from mcp.client.sse import sse_client

async with sse_client("https://mcp-perturbation-ondu7mwjpa-uc.a.run.app/sse") as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()
        tools = await session.list_tools()
        print(f"Available tools: {len(tools.tools)}")
        for tool in tools.tools:
            print(f"  - {tool.name}")
```

### Option 3: Use with Streamlit UI

Configure the Streamlit UI to connect to the remote server:

```bash
export MCP_SERVER_URL="https://mcp-perturbation-ondu7mwjpa-uc.a.run.app/sse"
cd ui/jupyter-notebook
streamlit run streamlit_app.py
```

---

## 🎯 Available Tools (8 GEARS Tools)

The deployed server provides the following tools:

1. **perturbation_load_dataset** - Load scRNA-seq data from GEO or .h5ad
2. **perturbation_setup_model** - Initialize GEARS GNN model
3. **perturbation_train_model** - Train GEARS on 20 epochs
4. **perturbation_compute_delta** - Calculate perturbation effects
5. **perturbation_predict_response** - Predict treatment outcomes
6. **perturbation_differential_expression** - Find changed genes
7. **perturbation_get_latent** - Extract GNN embeddings
8. **perturbation_visualize** - Generate PCA/UMAP plots

---

## 📈 Expected Performance

| Operation | Response Time | Notes |
|-----------|--------------|-------|
| **Cold Start** | 5-15 seconds | First request after idle |
| **Warm Start** | <1 second | Subsequent requests |
| **Load Dataset** | 3-10 seconds | Depends on dataset size |
| **Train Model** | 5-10 minutes | 20 epochs, ~5000 cells |
| **Predict** | 2-5 seconds | Per prediction |

---

## 💰 Cost Estimates

**Current Configuration** (4Gi memory, 2 CPU):
- **Idle**: $0/hour (scales to zero)
- **Active**: ~$0.09/hour
- **Cold start**: Free (startup time not billed)

**Typical Usage** (1 patient prediction/day):
- Training: 10 min × $0.09/hour = **$0.015**
- Prediction: 5 sec × $0.09/hour = **$0.0001**
- **Total per patient**: ~**$0.02**

---

## 🚀 Next Steps

### Immediate (Now):
- [x] Deployment successful
- [ ] Test with Claude Desktop
- [ ] Run full GEARS workflow with patient data
- [ ] Verify predictions are accurate

### Short-term (This Week):
- [ ] Monitor Cloud Run costs
- [ ] Set up logging/monitoring dashboard
- [ ] Test with real PatientOne data
- [ ] Benchmark performance vs local execution

### Long-term (Future):
- [ ] Consider GPU upgrade for faster training
- [ ] Production deployment with authentication
- [ ] VPC integration for private datasets
- [ ] Auto-scaling optimization

---

## 🔧 Troubleshooting

### If Claude Desktop doesn't see the server:

1. Check config file syntax (valid JSON)
2. Restart Claude Desktop completely
3. Check logs: `~/Library/Logs/Claude/mcp*.log`
4. Verify URL is correct: `https://mcp-perturbation-ondu7mwjpa-uc.a.run.app/sse`

### If predictions fail:

1. Check Cloud Run logs:
   ```bash
   gcloud logging read \
     "resource.type=cloud_run_revision AND resource.labels.service_name=mcp-perturbation" \
     --limit 50 --project precision-medicine-poc
   ```

2. Verify environment variables:
   ```bash
   gcloud run services describe mcp-perturbation \
     --region us-central1 \
     --project precision-medicine-poc
   ```

3. Check if instance is running (not scaled to zero):
   ```bash
   gcloud run revisions list --service mcp-perturbation \
     --region us-central1 --project precision-medicine-poc
   ```

---

## 📚 Documentation

- [README.md](README.md) - Main documentation
- [ARCHITECTURE.md](ARCHITECTURE.md) - System architecture with diagrams
- [DEPLOYMENT_TESTING.md](DEPLOYMENT_TESTING.md) - Comprehensive testing guide
- [TESTING_README.md](TESTING_README.md) - Quick start testing
- [GEARS_MIGRATION_SUMMARY.md](GEARS_MIGRATION_SUMMARY.md) - Migration from scgen
- [ALTERNATIVES_COMPARISON.md](ALTERNATIVES_COMPARISON.md) - Framework comparison

---

## ✅ Deployment Checklist

- [x] Code migrated from scgen to GEARS
- [x] Dependencies updated (fastmcp>=0.2.0)
- [x] Import paths fixed (from fastmcp import FastMCP)
- [x] Dockerfile created with Python 3.11
- [x] Environment variables configured
- [x] Container deployed to Cloud Run
- [x] Server started successfully
- [x] SSE endpoint responding
- [x] Logs show no errors
- [x] Service serving traffic (100%)
- [ ] Tested with Claude Desktop
- [ ] Full workflow validated
- [ ] Performance benchmarked

---

## 🎊 Success!

The mcp-perturbation server is **now live and ready to use** for predicting cancer treatment responses using GEARS graph neural networks!

**Service URL**: https://mcp-perturbation-ondu7mwjpa-uc.a.run.app

Try it with Claude Desktop or your Streamlit UI and start predicting treatment responses! 🚀

---

**Last Updated**: 2026-01-21
**Deployment Status**: ✅ **OPERATIONAL**
**Version**: 0.2.0 (GEARS-based)
