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AI Protocols

Mastering MCP: The Standard for AI Interoperability

October 28, 2025•12 min read
#MCP#Anthropic#Standard#Open Source
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Arya Teja Rudraraju
AI Product Manager & Engineering Lead
Mastering MCP: The Standard for AI Interoperability

One of the biggest friction points in building AI agents has been the N+1 Tool Problem. You want your agent to access Google Drive? Write a custom connector. Slack? Another specific API wrapper. A local Postgres DB? Yet another implementation.

Enter MCP (Model Context Protocol).

What is MCP?

MCP is an open standard that creates a universal language for AI models to connect to data and tools. Think of it like USB-C for AI. Instead of every AI app (Claude Desktop, Cursor, Zed) needing to write a specific driver for every tool (Postgres, Linear, GitHub), they just need to speak MCP.

The Architecture

MCP operates on a Client-Host-Server model:

  • MCP Hosts: Applications like Claude Desktop or IDEs that want to access data.
  • MCP Clients: The protocol layer that maintains the connection (often 1:1 with Hosts).
  • MCP Servers: Lightweight processes that expose specific capabilities (Resources, Prompts, Tools).

Why This Changes Everything

  1. Write Once, Run Everywhere: If you build an MCP server for your internal company API, it instantly works with Claude Desktop, Cursor, and any future MCP-compliant agent.
  2. Security boundaries: MCP servers run locally or in controlled environments. You don't need to give your OpenAI API key full access to your production database; you give the local MCP server access, and the model only sees what the server exposes.
  3. Local Context: It enables models to interact with your local environment (files, localhost servers) securely, which was previously a huge hurdle for cloud-based LLMs.

Building Your First Server

I recently built a simple MCP server to query my local dev database. It took <100 lines of TypeScript.

// A snippet of an MCP tool definition
const server = new McpServer({
  name: "db-query",
  version: "1.0.0"
});

server.tool("execute_sql", { sql: z.string() }, async ({ sql }) => {
  // secure execution logic
  return { content: [{ type: "text", text: result }] };
});

Conclusion

MCP is the missing link that moves us from "Chatbots" to "Assistants that can actually do things." If you are building AI tools today, you need to be looking at MCP.