MCP 服务器

Azure Platform Engineering MCP Server

Provides AI agents with real-time access to live Azure infrastructure, including AKS cluster health, resource management, policy validation, and Terraform analysis through a Model Context Protocol interface.

README

Azure Platform Engineering MCP Server

A Model Context Protocol (MCP) server connected to real Azure infrastructure. Deploys a live AKS cluster and provides AI agents with real-time access to cluster health, Azure resources, policy validation, and Terraform analysis.

No simulated data. Every API call hits live Azure services.

What This Does

This MCP server gives AI agents (Claude Code, GitHub Copilot, etc.) structured access to a real Azure platform:

Tool	Data Source	What It Returns
`check_cluster_health`	Kubernetes API (live AKS)	Real node status, pod health, events, recommendations
`validate_manifest`	PyYAML parser	Policy compliance for real YAML manifests
`analyze_terraform`	HCL analysis	Quality score for Terraform modules
`query_azure_resources`	Azure Resource Manager API	Real resource groups, AKS clusters, VMs

Architecture

Architecture Overview

How It Works

MCP Protocol Flow

Deployment Pipeline

Live Proof

This was deployed and tested against a real AKS cluster on Azure for Students.

kubectl get nodes

kubectl get pods (with real CrashLoopBackOff)

kubectl get pods

MCP Server Test Output (LIVE)

Live test output

Azure Portal

Infrastructure

Deployed via Terraform in terraform/main.tf:

Resource	Details
Resource Group	`mcp-demo-rg` (Sweden Central)
AKS Cluster	`aks-platform-demo` — Kubernetes 1.32
Node Pool	1x Standard_D2as_v4 (cost-optimised)
Network	Azure CNI + Calico network policy
Monitoring	Log Analytics workspace + OMS agent
Workloads	3 namespaces: product-api, product-web, platform-monitoring

The platform-monitoring namespace includes an intentionally failing pod to demonstrate how the health check tool detects and reports real CrashLoopBackOff incidents.

Setup

Prerequisites

Azure subscription (Azure for Students works)
Azure CLI (az)
Terraform
kubectl
Python 3.10+

Deploy

# 1. Clone
git clone https://github.com/sat0ps/mcp-server-azure-platform.git
cd mcp-server-azure-platform

# 2. Login to Azure
az login

# 3. Deploy infrastructure
cd terraform
terraform init
terraform plan
terraform apply

# 4. Configure kubectl
az aks get-credentials --resource-group mcp-demo-rg --name aks-platform-demo

# 5. Deploy sample workloads
kubectl apply -f workloads.yaml

# 6. Install dependencies
cd ..
pip install -r requirements.txt

# 7. Set subscription ID
export AZURE_SUBSCRIPTION_ID=$(az account show --query id -o tsv)

# 8. Test
python test_tools.py

Tear Down (stop costs)

cd terraform
terraform destroy

Live Test Output

Platform Engineering MCP Server — Live Test Suite
============================================================
Testing against REAL Azure infrastructure

TEST: Initialize
  Server: azure-platform-engineering-mcp v1.0.0
  PASS

TEST: Check Cluster Health (LIVE)
  Source: LIVE — Kubernetes API
  Health Score: 100 (healthy)
  Nodes: 1/1 ready
  Pods: 25 running, 0 pending, 0 failed
  Problem pods:
    - platform-monitoring/failing-service: 6 restarts (CrashLoopBackOff)
  PASS

TEST: Query Azure Resources (LIVE)
  Source: LIVE — Azure Resource Manager API
  AKS Clusters found: 1
    - aks-platform-demo (swedencentral) — K8s 1.32 — Succeeded
      Pool: system — Standard_D2as_v4 x1
  PASS

ALL TESTS PASSED (Live Azure)

Diagrams

Diagram	What It Shows
Architecture Overview	Full system: agent → MCP → live tools → real Azure
MCP Protocol Flow	Real request flow with CrashLoopBackOff detection
Deployment Pipeline	terraform apply → kubectl → test → destroy
Incident Detection	How MCP found the failing-service pod
Policy Validation	YAML validation gate with real PyYAML parsing
Agent Configuration	How agents.md + skills + instructions work together
Terraform PR Review	AI-driven PR review with quality scoring

Project Structure

├── mcp_server.py              # MCP server (JSON-RPC 2.0 over stdio)
├── tools/
│   ├── kubernetes_live.py     # Real K8s API health checks
│   ├── compliance_live.py     # Real YAML policy validation
│   ├── terraform_live.py      # Terraform HCL analysis
│   ├── azure_resources.py     # Real Azure Resource Manager queries
│   └── azure_metrics.py       # Azure Monitor metrics (SDK v2 WIP)
├── terraform/
│   ├── main.tf                # AKS + monitoring infrastructure
│   └── workloads.yaml         # Sample K8s deployments
├── .claude/
│   ├── agents.md              # Agent behaviour configuration
│   └── mcp.json               # Claude Code MCP config
├── skills/
│   └── SKILL.md               # Repeatable agent procedures
├── instructions/
│   ├── terraform-standards.md # Terraform quality standards
│   └── kubernetes-standards.md# K8s deployment requirements
├── scripts/
│   └── setup.sh               # Automated deployment script
├── diagrams/                  # Architecture diagrams + live screenshots
├── requirements.txt           # Python dependencies
└── test_tools.py              # Live test suite