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Stores and queries resource lifecycle telemetry, deployment events, heartbeats, and cost analysis data for multi-cloud GPU infrastructure management. Manages task queues for orchestrating evaluation, deployment, and on-premises agents in the multi-cloud platform workflow.

How do I use Multi-Cloud Infrastructure MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Multi-Cloud Infrastructure MCP Server deploy an inference server with auto GPU selection" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Multi-Cloud Infrastructure MCP Server

by nomadslayer

Overview Schema Related Servers Score Discussions

Python

Hybrid

MCP (Multi-Cloud Platform) Server

This repository provides a working, extensible reference implementation of an MCP server with multiple agent types and a SkyPilot-backed autoscaling/deployment path. It now includes integration hooks to report resource lifecycle and telemetry to an "AI Envoy" endpoint (a generic HTTP ingestion endpoint).

Highlights

Evaluation Agent (prompt + rules) reads tasks from Redis and outputs resource plans.
SkyPilot Agent builds dynamic YAML and executes the sky CLI.
OnPrem Agent acts to run on-prem deployments (placeholder using kubectl/helm).
Orchestrator wires agents together using Redis queues and ClickHouse telemetry.
Pluggable LLM client - default configured to call a local LiteLLM gateway for minimax-m1.
Phoenix observability hooks and Envoy integration for telemetry events.

Additional files

scripts/resource_heartbeat.py — example script that runs inside a provisioned resource and posts periodic GPU utilization/heartbeat to the orchestrator.

Quick start (local dry-run)

Install Python packages: pip install -r requirements.txt
Start Redis (e.g. docker run -p 6379:6379 -d redis) and optionally ClickHouse.
Start the MCP server: python -m src.mcp.main
Push a demo task into Redis (see scripts/run_demo.sh)
Verify telemetry is forwarded to Phoenix and Envoy endpoints (configurable in .env).

Notes & caveats

This is a reference implementation. You will need to install and configure real services (SkyPilot CLI, LiteLLM/minimax-m1, Phoenix, and the Envoy ingestion endpoint) to get a fully working pipeline.

MCP Orchestrator - Quick Reference

🚀 Installation (5 minutes)

# 1. Configure environment
cp .env.example .env
nano .env  # Add your API keys

# 2. Deploy everything
chmod +x scripts/deploy.sh
./scripts/deploy.sh

# 3. Verify
curl http://localhost:8000/health

📡 Common API Calls

Deploy with Auto GPU Selection

# Inference workload (will select cost-effective GPU)
curl -X POST http://localhost:8000/api/v1/providers/runpod/deploy \
  -H "Content-Type: application/json" \
  -d '{
    "task_type": "inference",
    "spec": {
      "name": "llm-server",
      "image": "vllm/vllm-openai:latest",
      "command": "python -m vllm.entrypoints.api_server"
    }
  }'

# Training workload (will select powerful GPU)
curl -X POST http://localhost:8000/api/v1/providers/vastai/deploy \
  -H "Content-Type: application/json" \
  -d '{
    "task_type": "training",
    "spec": {
      "name": "fine-tune-job",
      "image": "pytorch/pytorch:latest"
    }
  }'

Deploy with Specific GPU

curl -X POST http://localhost:8000/api/v1/providers/runpod/deploy \
  -H "Content-Type: application/json" \
  -d '{
    "spec": {
      "name": "custom-pod",
      "gpu_name": "RTX 4090",
      "resources": {
        "accelerators": "RTX 4090:2"
      }
    }
  }'

Deploy to Provider (Default: ON_DEMAND + RTX 3060)

curl -X POST http://localhost:8000/api/v1/providers/runpod/deploy \
  -H "Content-Type: application/json" \
  -d '{"spec": {"name": "simple-pod"}}'

Register Existing Infrastructure

# Vast.ai instance
curl -X POST http://localhost:8000/api/v1/register \
  -H "Content-Type: application/json" \
  -d '{
    "provider": "vastai",
    "resource_id": "12345",
    "credentials": {"api_key": "YOUR_VASTAI_KEY"}
  }'

# Bulk registration
curl -X POST http://localhost:8000/api/v1/register \
  -H "Content-Type: application/json" \
  -d '{
    "provider": "vastai",
    "resource_ids": ["12345", "67890"],
    "credentials": {"api_key": "YOUR_VASTAI_KEY"}
  }'

List Resources

# All RunPod resources
curl http://localhost:8000/api/v1/providers/runpod/list

# All Vast.ai resources
curl http://localhost:8000/api/v1/providers/vastai/list

Terminate Resource

curl -X POST http://localhost:8000/api/v1/providers/runpod/delete/pod_abc123

🎯 GPU Rules Management

View Rules

curl http://localhost:8000/api/v1/gpu-rules

Add Rule

curl -X POST http://localhost:8000/api/v1/gpu-rules \
  -H "Content-Type: application/json" \
  -d '{
    "gpu_family": "H100",
    "type": "Enterprise",
    "min_use_case": "large-scale training",
    "optimal_use_case": "foundation models",
    "power_rating": "700W",
    "typical_cloud_instance": "RunPod",
    "priority": 0
  }'

Delete Rule

curl -X DELETE http://localhost:8000/api/v1/gpu-rules/RTX%203060

🔍 Monitoring

ClickHouse Queries

-- Active resources
SELECT provider, status, count() as total
FROM resources
WHERE status IN ('running', 'active')
GROUP BY provider, status;

-- Recent deployments
SELECT *
FROM deployments
ORDER BY created_at DESC
LIMIT 10;

-- Latest heartbeats
SELECT resource_id, status, timestamp
FROM heartbeats
WHERE timestamp > now() - INTERVAL 5 MINUTE
ORDER BY timestamp DESC;

-- Cost analysis
SELECT
    provider,
    sum(price_hour) as total_hourly_cost,
    avg(price_hour) as avg_cost
FROM resources
WHERE status = 'running'
GROUP BY provider;

-- Event volume
SELECT
    event_type,
    count() as count,
    toStartOfHour(timestamp) as hour
FROM events
WHERE timestamp > now() - INTERVAL 24 HOUR
GROUP BY event_type, hour
ORDER BY hour DESC, count DESC;

View Logs

# All services
docker-compose logs -f

# API only
docker-compose logs -f mcp-api

# Heartbeat monitor
docker-compose logs -f heartbeat-worker

# ClickHouse
docker-compose logs -f clickhouse

🛠️ Maintenance

Restart Services

# Restart all
docker-compose restart

# Restart API only
docker-compose restart mcp-api

# Reload with new code
docker-compose up -d --build

Backup ClickHouse

# Backup database
docker-compose exec clickhouse clickhouse-client --query \
  "BACKUP DATABASE mcp TO Disk('default', 'backup_$(date +%Y%m%d).zip')"

# Export table
docker-compose exec clickhouse clickhouse-client --query \
  "SELECT * FROM resources FORMAT CSVWithNames" > resources_backup.csv

Clean Up

# Stop all services
docker-compose down

# Stop and remove volumes (WARNING: deletes data)
docker-compose down -v

# Prune old data from ClickHouse (events older than 90 days auto-expire)
docker-compose exec clickhouse clickhouse-client --query \
  "OPTIMIZE TABLE events FINAL"

🐛 Troubleshooting

Service won't start

# Check status
docker-compose ps

# Check logs
docker-compose logs mcp-api

# Verify config
cat .env | grep -v '^#' | grep -v '^$'

ClickHouse connection issues

# Test connection
docker-compose exec clickhouse clickhouse-client --query "SELECT 1"

# Reinitialize
docker-compose exec clickhouse clickhouse-client --multiquery < scripts/init_clickhouse.sql

# Check tables
docker-compose exec clickhouse clickhouse-client --query "SHOW TABLES FROM mcp"

API returns 404 for provider

# Check if agent initialized
docker-compose logs mcp-api | grep -i "AgentRegistry initialized"

# Restart with fresh logs
docker-compose restart mcp-api && docker-compose logs -f mcp-api

Heartbeat not working

# Check heartbeat worker
docker-compose logs heartbeat-worker

# Manual health check
curl http://localhost:8000/api/v1/providers/runpod/list

📝 Environment Variables

Key variables in .env:

# Required
RUNPOD_API_KEY=xxx          # Your RunPod API key
VASTAI_API_KEY=xxx          # Your Vast.ai API key (used per-request only)

# ClickHouse
CLICKHOUSE_PASSWORD=xxx     # Set strong password

# Optional
LOG_LEVEL=INFO              # DEBUG for verbose logs
WORKERS=4                   # API worker processes
HEARTBEAT_INTERVAL=60       # Seconds between health checks

🔐 Security Checklist

Change default ClickHouse password
Store .env securely (add to .gitignore)
Use separate API keys for prod/staging
Enable ClickHouse authentication
Configure AI Envoy Gateway policies
Rotate API keys regularly
Review ClickHouse access logs
Set up alerting for unhealthy resources

📚 Resources

API Docs: http://localhost:8000/docs
ClickHouse UI: http://localhost:8124 (with --profile debug)
Health Check: http://localhost:8000/health
Full README: See README.md

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