Model Context Protocol (MCP)

AI & Machine Learning intermediate

The Model Context Protocol (MCP) is an open standard that lets AI assistants connect to external tools, data sources, and services through a uniform interface, making it the de-facto integration layer for modern AI agents.

Summary

The Model Context Protocol (MCP) is an open, vendor-neutral standard that defines how AI assistants discover and call external tools, read resources, and access data sources through a single uniform interface—removing the need for a custom integration per model and per tool.

What is the Model Context Protocol?

MCP was open-sourced by Anthropic in November 2024 and later donated to the Linux Foundation, signalling its shift from a single-vendor project to an industry standard. At its core it is a simple JSON-RPC protocol: an MCP server exposes tools and resources with machine-readable schemas, and an MCP client (the AI assistant or agent) discovers and invokes them at runtime.

This separation means a tool only has to be exposed once, via an MCP server, to become usable by any MCP-capable assistant. It addresses two questions: which capabilities are available, and—through its OAuth 2.1 / OpenID Connect-based authorization layer—how access to those capabilities is authorized and what the user has consented to. The decision about what an agent is ultimately allowed to do remains with the host application and the human operating it.

MCP is commonly paired with the A2A protocol: MCP covers the agent-to-tool axis, while A2A covers communication between agents.

Why is MCP relevant?

  • Integration standard: One protocol replaces bespoke per-tool, per-model connectors, reducing integration effort
  • Vendor neutrality: Governed under the Linux Foundation rather than owned by a single vendor, lowering lock-in risk
  • Identity-aware: The authorization layer aligns with OAuth 2.1 and OpenID Connect, fitting existing enterprise identity stacks
  • Ecosystem reach: Adopted broadly across coding agents and platforms, making it a safe foundation for agentic AI architectures

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