SKILL.md

--- frontmatter

name: mcp-builder:deploy
description: Generate deployment configuration for Cloud Run, Render, or Fly.io

Deploy MCP Server

You are helping the user deploy their MCP server to production.

Deployment Options

Platform	Best For	Complexity	Cost
Google Cloud Run	Auto-scaling, enterprise	Medium	Pay-per-use
Render	Simple deploys, small teams	Low	$7+/month
Fly.io	Edge deployment, low latency	Medium	Pay-per-use

Workflow

Phase 1: Choose Platform

Ask the user:

•What's your expected traffic volume?
•Do you need edge deployment (low latency globally)?
•What's your budget?
•Do you already use a cloud provider?

Phase 2: Prepare for Deployment

Verify server is production-ready:

Google Cloud Run

Dockerfile

dockerfile

# Python FastMCP Server
FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY . .

# Cloud Run sets PORT environment variable
ENV PORT=8080
EXPOSE 8080

# Start server
CMD ["python", "server.py"]

requirements.txt

code

fastmcp>=0.1.0
uvicorn>=0.30.0
python-dotenv>=1.0.0
httpx>=0.27.0
# Add your other dependencies

Deploy Script

bash

#!/bin/bash
# deploy-cloud-run.sh

PROJECT_ID="your-project-id"
REGION="us-central1"
SERVICE_NAME="your-mcp-server"

# Build and push image
gcloud builds submit --tag gcr.io/$PROJECT_ID/$SERVICE_NAME

# Deploy to Cloud Run
gcloud run deploy $SERVICE_NAME \
  --image gcr.io/$PROJECT_ID/$SERVICE_NAME \
  --platform managed \
  --region $REGION \
  --allow-unauthenticated \
  --set-env-vars "NODE_ENV=production" \
  --memory 512Mi \
  --cpu 1 \
  --min-instances 0 \
  --max-instances 10

# Get the URL
gcloud run services describe $SERVICE_NAME \
  --region $REGION \
  --format 'value(status.url)'

Cloud Run Tips

•Set --min-instances 1 to avoid cold starts
•Use --cpu-boost for faster cold starts
•Set --concurrency based on your server's capacity
•Disable HTTP/2 if you have issues: --use-http2=false

Render

render.yaml

yaml

services:
  - type: web
    name: your-mcp-server
    runtime: python
    buildCommand: pip install -r requirements.txt
    startCommand: python server.py
    envVars:
      - key: PORT
        value: 10000
      - key: NODE_ENV
        value: production
      - key: PYTHON_VERSION
        value: 3.11.0
    healthCheckPath: /health
    autoDeploy: true

Deploy Steps

•Push code to GitHub
•Connect repo to Render
•Render auto-detects render.yaml
•Set environment variables in dashboard
•Deploy

Render Tips

•Use "Web Service" type, not "Background Worker"
•Health check keeps service awake
•Auto-deploy on push to main branch

Fly.io

fly.toml

toml

app = "your-mcp-server"
primary_region = "iad"

[build]
  dockerfile = "Dockerfile"

[http_service]
  internal_port = 8080
  force_https = true
  auto_stop_machines = true
  auto_start_machines = true
  min_machines_running = 0

[[vm]]
  cpu_kind = "shared"
  cpus = 1
  memory_mb = 512

Dockerfile (same as Cloud Run)

dockerfile

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
ENV PORT=8080
EXPOSE 8080
CMD ["python", "server.py"]

Deploy Script

bash

#!/bin/bash
# deploy-fly.sh

# Install Fly CLI if needed
# curl -L https://fly.io/install.sh | sh

# Login
fly auth login

# Create app (first time only)
fly apps create your-mcp-server

# Set secrets
fly secrets set AUTH0_DOMAIN=your-domain.auth0.com
fly secrets set AUTH0_AUDIENCE=https://your-api.com

# Deploy
fly deploy

# Check status
fly status

Fly.io Tips

•Use --ha for high availability (2+ machines)
•Deploy to multiple regions for global low latency
•fly scale count 2 for redundancy

Environment Variables

Set these in your deployment:

bash

# Required
PORT=8080
NODE_ENV=production

# If using Auth
AUTH0_DOMAIN=your-tenant.auth0.com
AUTH0_AUDIENCE=https://your-api.com

# If using database
DATABASE_URL=postgresql://user:pass@host:5432/db

# Your API keys (keep secret!)
OPENAI_API_KEY=sk-...

Post-Deployment Checklist

• Health endpoint responds: curl https://your-server.com/health
• Tools list correctly: curl -X POST https://your-server.com/mcp ...
• Test one tool call end-to-end
• Configure custom domain (optional)
• Set up monitoring/alerts
• Test with real MCP client (Claude Desktop, Cursor)

Update State

After deployment, update .mcp-builder/state.json:

json

{
  "deployment": {
    "platform": "cloud-run",
    "url": "https://your-server-xyz.run.app",
    "region": "us-central1",
    "deployedAt": "2025-01-15T12:00:00Z"
  }
}