# 🚀 Quick Start Guide
Get the AEM Assets MCP Server running in minutes!
## Prerequisites Checklist
- [ ] Python 3.11+ installed
- [ ] AEM Assets API credentials ready
- [ ] Choose deployment platform (Vercel or Google Cloud Run)
## ⚡ 60-Second Local Setup
```bash
# 1. Run setup script
./setup.sh
# 2. Edit environment variables
nano .env
# 3. Run server
source venv/bin/activate
uvicorn app.main:app --reload
# 4. Test
curl http://localhost:8000/health
```
✅ **Done!** Server running at http://localhost:8000
## 🌐 Deploy to Vercel (Fastest)
```bash
# Install Vercel CLI
npm i -g vercel
# Deploy
vercel
# Add env vars in dashboard
# Deploy to production
vercel --prod
```
🎉 **Live!** Your API is now at `https://your-project.vercel.app/api/mcp`
## ☁️ Deploy to Cloud Run (Production-Ready)
```bash
# Set up GCP
gcloud config set project YOUR_PROJECT_ID
gcloud services enable run.googleapis.com artifactregistry.googleapis.com
# Create artifact registry
gcloud artifacts repositories create aem-mcp-images \
--repository-format=docker \
--location=us-central1
# Build and push
docker build -t us-central1-docker.pkg.dev/YOUR_PROJECT/aem-mcp-images/aem-mcp-server:latest .
docker push us-central1-docker.pkg.dev/YOUR_PROJECT/aem-mcp-images/aem-mcp-server:latest
# Deploy
cd terraform
cp terraform.tfvars.example terraform.tfvars
nano terraform.tfvars # Edit with your values
terraform init
terraform apply
```
🚀 **Deployed!** Get URL with `terraform output service_url`
## 🤖 Connect to ChatGPT
1. Create Custom GPT at https://chat.openai.com
2. Add Action with this OpenAPI schema:
```json
{
"openapi": "3.1.0",
"info": {
"title": "AEM Assets MCP Server",
"version": "1.0.0"
},
"servers": [
{"url": "https://your-deployment-url.com"}
],
"paths": {
"/api/mcp": {
"post": {
"operationId": "callTool",
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"tool": {
"type": "string",
"enum": ["list_folders", "list_published_assets", "search_assets", "list_assets_by_folder", "bulk_update_metadata", "list_assets_by_creator", "list_all_assets", "get_asset_details"]
},
"arguments": {"type": "object"}
},
"required": ["tool"]
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {"type": "object"}
}
}
}
}
}
}
}
}
```
3. Test with: *"List all folders in the root directory"*
## 📚 Full Documentation
- **[README.md](./README.md)** - Complete overview
- **[LOCAL_DEVELOPMENT.md](./LOCAL_DEVELOPMENT.md)** - Development guide
- **[DEPLOYMENT_VERCEL.md](./DEPLOYMENT_VERCEL.md)** - Phase 1 deployment
- **[DEPLOYMENT_CLOUDRUN.md](./DEPLOYMENT_CLOUDRUN.md)** - Phase 2 deployment
- **[CHATGPT_SETUP.md](./CHATGPT_SETUP.md)** - ChatGPT integration
## 🆘 Troubleshooting
### Server won't start
```bash
# Check Python version
python3 --version # Should be 3.11+
# Reinstall dependencies
pip install -r requirements.txt
# Check .env file
cat .env
```
### Import errors
```bash
# Make sure you're in project root
pwd
# Activate virtual environment
source venv/bin/activate
```
### Environment variables missing
```bash
# Make sure .env exists and has values
cp env.example .env
nano .env
```
### AEM connection errors
- Verify access token is valid (they expire!)
- Check base URL is correct
- Ensure Organization ID format: `xxxxx@AdobeOrg`
## 💡 Example Prompts for ChatGPT
Once connected:
- ✅ *"List all folders in the root directory"*
- ✅ *"Show me published assets"*
- ✅ *"Find assets related to Electric Vehicle"*
- ✅ *"Update all assets in /content/dam/products with jancode: ABC123"*
- ✅ *"List assets uploaded by user@example.com"*
## 🎯 Next Steps
1. ✅ Get it running locally
2. ✅ Deploy (Vercel for quick start, Cloud Run for production)
3. ✅ Connect to ChatGPT
4. ✅ Test with your AEM instance
5. ✅ Customize for your needs
---
**Questions?** Check the full documentation or open an issue!
