What is MCP, and why every AI engineer should care

The Model Context Protocol (MCP) is an open standard for connecting LLM applications to external tools, data sources, and systems — the same way LSP standardized how editors talk to language tooling.

The problem MCP solves

Before MCP, every agent framework wired up tools its own way. A tool built for one framework couldn’t be reused in another without a rewrite. That meant:

• Duplicated integration work across every agent project
• No shared ecosystem of reusable tools
• Vendor lock-in disguised as “framework choice”

How it works

An MCP server exposes tools, resources, and prompts over a standard protocol. Any MCP-compatible client — Claude Code, Claude Desktop, or a custom agent — can discover and call those tools without framework-specific glue code.

from mcp.server import Server

server = Server("my-tools")

@server.tool()
async def search_docs(query: str) -> str:
    """Search internal documentation."""
    return run_search(query)

Where I use it in production

At TCS, I’ve used MCP tool-calling as part of multi-agent workflows built on the Microsoft Foundry SDK — letting agents call into internal systems through a consistent interface rather than bespoke API wrappers for every integration.

Takeaway

If you’re building agentic systems today, treat MCP the way you’d treat any protocol decision: standardizing early saves you from re-plumbing every tool integration later.