Model Context Protocol (MCP)¶

The Model Context Protocol (MCP) is an open standard that lets AI models securely connect to external tools, data, and services in a consistent way. It’s like a universal adapter that gives AI real‑time context and the ability to take action.

Introduction to MCP¶

MCP is an open standard for connecting AI models to external tools, data, and services in a secure, structured way. It lets AI assistants go beyond static knowledge and interact with live systems.

Core components:

Host: The AI environment (e.g., your chat app).
Client: Middleware that discovers and manages MCP servers.
Server: The component that exposes capabilities or data to the AI.

The Role of MCP Servers¶

Wraps an external capability (API, database, file system, cloud service) and exposes it via MCP’s JSON‑RPC interface.

Responsibilities:

Define available methods and their parameters.
Validate inputs and outputs.
Handle authentication and authorization.
Return structured, predictable responses.

Your First MCP Server — “Hello, World”¶

A server that returns the current time.

{
  "methods": {
    "get_time": {
      "description": "Returns the current system time",
      "params": {},
      "returns": {
        "type": "string",
        "description": "Current time in ISO 8601 format"
      }
    }
  }
}

Key learning: Method definition, no parameters, simple return type.

Adding Parameters and Logic¶

How to accept inputs and process them. For example, currency converter server.

{
  "methods": {
    "convert_currency": {
      "description": "Converts an amount from one currency to another",
      "params": {
        "amount": {"type": "number"},
        "from": {"type": "string"},
        "to": {"type": "string"}
      },
      "returns": {
        "type": "number",
        "description": "Converted amount"
      }
    }
  }
}

Key learning: Input validation, branching logic, external API calls.

Intermediate — Connecting to Cloud APIs¶

Integrate with a real service. For example, Azure Resource Graph query for Network Security Groups (NSGs).

{
  "methods": {
    "list_nsgs": {
      "description": "Lists all Network Security Groups in Azure",
      "params": {
        "subscription_id": {"type": "string"}
      },
      "returns": {
        "type": "array",
        "items": {"type": "object"}
      }
    }
  }
}

Key learning: Authentication (Azure AD), pagination, rate limits.

Advanced — Multi‑Source Orchestration¶

Combine multiple MCP servers for richer workflows.

Scenario: NSG compliance check.
Azure Inventory Server: Lists NSGs and rules.
Docs Intelligence Server: Summarizes Microsoft best practices.
Policy Engine Server: Compares NSG configs to best practices and returns pass/fail.

Flow:

Host calls Inventory → Docs → Policy Engine.
AI merges results into a compliance report.

Real‑World Scenario — IaC + Live Resource Scan¶

Apply MCP to security and DevOps. For example, detect Terraform‑defined NSGs that violate policy before deployment.

IaC Scanner Server: Parses Terraform files for NSG definitions.
Policy Engine Server: Evaluates them against the same controls used for live Azure NSGs.
Outcome: Prevents insecure deployments.

Best Practices for MCP Server Design¶

Clarity: Use descriptive method names and docstrings.
Validation: Enforce strict input/output schemas.
Security: Never expose secrets; use secure auth flows.
Versioning: Tag server definitions and methods.
Observability: Log calls, errors, and performance metrics.
Modularity: Keep servers focused; one capability per server when possible.

Summary¶

By starting with a simple “Hello, World” server and layering in parameters, external API calls, and multi‑server orchestration, you can design MCP servers that:

Pull live data from cloud platforms.
Cross‑reference it with authoritative documentation.
Apply policy logic to produce actionable insights.