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The Model Context Protocol (MCP) Standard
Model Context Protocol (MCP) & Tools • Protocol Architecture9 min read

The Model Context Protocol (MCP) Standard

Understanding the open standard created by Anthropic. Hosts, Clients, Servers, and Stdio vs SSE/HTTP.

Practice in Prompt LabTest in AI Interview

Core Conceptual Brief

Before MCP, every AI agent used bespoke, fragmented tool calling integrations. Anthropic open-sourced MCP as the USB-C of AI tool connectivity. Master the protocol lifecycle.

Theoretical & Empirical Architecture

The Model Context Protocol (MCP) is an open standard designed by Anthropic that standardizes how AI applications (Hosts) securely discover and connect to external data sources, internal tools, and developer environments (Servers).

Instead of hardcoding custom OpenAI or Anthropic function-calling schemas for every database, Git provider, or file system, developers write an MCP Server once, and any compatible Host (Claude Desktop, Cursor, Antigravity, VS Code) connects instantly.

Under the Hood: Key Engineering Mechanics

1The MCP Topology: Host, Client & Server

A Host is the user-facing AI application (e.g. Claude Desktop). The Client is the internal protocol connector within the host that initiates connections. The Server is a standalone process exposing tools, prompts, and resources.

2Transports: Stdio vs SSE / HTTP

Stdio Transport: The client spawns the server as a local child process and communicates via standard input/output streams (stdin/stdout). Highly secure, zero open network ports. SSE (Server-Sent Events) Transport: Operates over HTTP for remote microservices, cloud deployments, and distributed team setups.

3JSON-RPC 2.0 Foundation

All MCP messages are formatted as strict JSON-RPC 2.0 payloads: every request includes jsonrpc: "2.0", an integer id, a method name, and params. Responses return either result or error.

MCP Protocol Trace: Handshake & Tool Discovery

// 1. Client -> Server: Initialize Handshake
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "initialize",
  "params": {
    "protocolVersion": "2024-11-05",
    "capabilities": {"roots": {"listChanged": true}},
    "clientInfo": {"name": "DataArenaHost", "version": "1.0.0"}
  }
}

// 2. Server -> Client: Initialize Result
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "protocolVersion": "2024-11-05",
    "capabilities": {"tools": {}, "resources": {}},
    "serverInfo": {"name": "PostgresMcpServer", "version": "2.1.0"}
  }
}

// 3. Client -> Server: Request Available Tools
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/list",
  "params": {}
}

Production Guardrails & Takeaways

  • MCP provides a universal JSON-RPC 2.0 standard for connecting LLMs to data and tools.
  • Stdio transport provides sandboxed security for local processes; SSE provides remote capability.
  • Building an MCP server ensures compatibility across Claude Desktop, Cursor, and custom agent runtimes.

Real Staff AI Systems Interview Question

Q: Why did the AI industry need MCP when OpenAI Function Calling already existed?

Staff Answer:

OpenAI Function Calling only standardizes how an LLM formats a JSON tool call; it does not standardize how tools are discovered, authenticated, transported, or shared across applications. Every application had to rebuild connectors for PostgreSQL, GitHub, Jira, etc. MCP standardizes the entire client-server lifecycle, creating an open ecosystem where any tool server works with any host.

The Model Context Protocol (MCP) Standard | AI Systems Engineering Guide · DataArena