What Is MCP?

The Problem MCP Was Built to Solve

AI language models are, by default, isolated. They know what they were trained on, and they can reason over text you paste into a conversation — but they can't reach into your database, read files on your computer, or call a live API unless someone builds a custom bridge every single time.

Before MCP, that bridge was function calling — a feature offered by providers like OpenAI, Anthropic, and Google that lets a model invoke pre-defined functions. Function calling works, but it has a fundamental limitation: each provider implements it differently. A tool you build for OpenAI's API needs to be reimplemented for Anthropic's, and again for Gemini's. Every integration is bespoke, tightly coupled to a single provider, and non-transferable.

The deeper problem is fragmentation. As AI moves from chat interfaces into production systems, developers are wiring up dozens of integrations — databases, code execution environments, file systems, external APIs — and doing it over and over again for each model they want to support.

What MCP Is

Model Context Protocol (MCP) is an open standard, created by Anthropic and released in November 2024, that defines a universal way for AI models to connect to external tools and data sources.

The simplest analogy: MCP is to AI integrations what HTTP is to the web. HTTP gave every web browser and server a shared language, which meant anyone could build a website that any browser could load. MCP gives AI models and external tools a shared language, which means anyone can build an MCP server that any MCP-compatible AI can use.

You write the integration once. Any AI host that supports MCP can use it — without modification.

What You Can Connect

MCP lets AI models interact with a wide range of external systems:

• File systems — read and write files on a local machine or server
• Databases — query PostgreSQL, SQLite, MySQL, and other stores
• APIs — call Slack, GitHub, Notion, Linear, or any HTTP API
• Browsers — control a headless browser for web scraping or automation
• Applications — interact with desktop apps or local processes
• Memory systems — store and retrieve information across sessions
• Code execution environments — run code and return results to the model

Open Source and Growing Adoption

MCP is fully open source. The specification, SDKs, and reference implementations are available on GitHub. This matters because it means no single company controls the protocol — anyone can implement it, extend it, or build tooling around it.

Adoption has been faster than most anticipated. Cursor, the AI code editor, adopted MCP early. A growing community of developers has published hundreds of MCP servers covering common integrations. Most significantly, OpenAI announced support for MCP in 2025, which effectively signals that the protocol is becoming an industry standard rather than a single-vendor solution.

The official MCP documentation and server registry live at [modelcontextprotocol.io](https://modelcontextprotocol.io).

Why It Matters Beyond the Technical Details

MCP shifts how we think about AI capabilities. Instead of "what can this model do," the question becomes "what can this model access." A model connected to a well-designed set of MCP servers can read your codebase, query your database, check your calendar, and file a GitHub issue — all within a single conversation.

This is the infrastructure layer that makes agentic AI practical. Agents need to act in the world: read real data, write real outputs, call real services. MCP provides a standardized way to give them that access without rebuilding the plumbing for every new model or every new tool.

It's early. The tooling is still maturing, and best practices for security and orchestration are still being established. But the direction is clear, and the adoption curve suggests MCP is becoming foundational infrastructure for the next generation of AI applications.