MCP — the Model Context Protocol — is an open standard, first released by Anthropic in November 2024, that lets an AI model connect to external tools, files, and data sources through one shared interface instead of a separate custom integration for every single combination of model and tool. It’s the reason an AI assistant can check your calendar, query a database, or run a script instead of only answering from what it was trained on.

If you’ve felt like “AI agents” went from a buzzword to something actually usable in the past year, MCP is a large part of why. It’s not a flashy feature — it’s plumbing. But plumbing is exactly what was missing.

The Problem MCP Actually Solves

Before MCP, connecting an AI model to an external system — a database, a file store, a project management tool, a search API — meant writing a custom integration for that specific model and that specific tool. Multiply that across many models and many tools, and you get what’s often called the M×N problem: M applications each needing their own connector to N different tools, where the number of integrations to build and maintain grows multiplicatively.

MCP turns that into an M+N problem instead. A tool builder implements MCP once (an “MCP server”), and any MCP-compatible AI application can use it without custom code. An AI application builder implements MCP support once (an “MCP client”), and it can talk to any MCP server that already exists. This is the same reason people describe MCP as the “USB-C port for AI” — a single, agreed-on plug instead of a different cable for every device.

How MCP Actually Works, in Plain Terms

An MCP setup has three pieces:

  • The host — the AI application itself (a chat assistant, a coding tool, an agent framework)
  • The client — the piece inside the host that speaks the MCP language
  • The server — a small program exposing a specific capability: a connection to a database, a file system, a calendar, an internal company API

Once connected, an MCP server can expose three basic things to the model: tools it can call (like “run this SQL query” or “create this ticket”), resources it can read (like a file or a document), and prompts that pre-package a useful instruction. The model doesn’t need to know how the database or the API actually works internally — it just needs to know MCP, and the server handles the translation.

This is genuinely useful groundwork if you’re building anything with AI agents — an agent is only as capable as the actions it can actually take, and MCP is what standardizes those actions across tools instead of locking you into whatever one platform happens to support.

MCP vs. an API vs. Function Calling — What’s Actually Different

This is where most confusion comes from, so it’s worth being direct: MCP isn’t a replacement for APIs, and it isn’t a new idea like function calling — it’s a standardized wrapper around both.

An API is how two pieces of software talk. Function calling is how a model tells an application “call this function with these arguments.” Both existed long before MCP. What MCP adds is a common shape for describing those functions and data sources so that any model can use any properly built server without a developer writing custom glue code for each pairing. It’s a protocol layer, not a competing technology to either one.

It’s also a different layer from something like retrieval-augmented generation (RAG). RAG is a technique for pulling relevant text into a model’s context before it answers. MCP is a connection standard the model can use to fetch that data — or take an action — in the first place. The two are often used together: an MCP server might be exactly what feeds a RAG pipeline its documents.

Why Every Major AI Company Ended Up Adopting It

MCP didn’t stay an Anthropic-only project for long. Within months of its November 2024 release, OpenAI announced it would adopt the same standard for connecting its models to data, and Google followed shortly after with its own commitment to support it. AWS and Microsoft both built out MCP support across their developer tooling, and IBM has published and open-sourced its own set of MCP servers connecting its enterprise products — file access, business workflow tools, and decision services — to any MCP-compatible AI agent.

The clearest signal of how far this went: in December 2025, Anthropic contributed MCP to a newly formed neutral home, the Agentic AI Foundation under the Linux Foundation — alongside Block’s “goose” and OpenAI’s “AGENTS.md” — with Google, Microsoft, AWS, Cloudflare, and Bloomberg backing the move. A company doesn’t hand its own standard over to neutral, vendor-agnostic governance unless competitors have already built their businesses on top of it and everyone needs guaranteed independence going forward. That’s a stronger adoption signal than any single company’s press release.

What This Actually Means If You’re Building With AI

For a developer or a business exploring AI tooling, the practical upside is straightforward: you’re no longer choosing between “pick one AI model and build everything custom around it” or “wait for official support.” If a tool has an MCP server — and a fast-growing number of tools do — you can connect it to whichever MCP-compatible AI application you’re using without writing an integration from scratch. This matters directly if you’re comparing agent frameworks or trying to decide between options like AutoGen, CrewAI, and LangGraph — most modern frameworks now support MCP as a way to plug in tools rather than requiring their own proprietary connector format.

It also lowers the cost of experimenting. Instead of committing engineering time to a bespoke integration before finding out whether an AI-powered workflow is actually useful, you can wire up an existing MCP server, test the idea, and decide whether it’s worth investing further. That’s a meaningfully different cost curve than what existed even a year ago, and it’s part of why multi-agent systems — several specialized agents working together with shared access to tools — have become practical to build without a large team.

Security and the Practical Trade-offs

Standardizing how a model reaches external systems also standardizes the ways it can go wrong, and it’s worth being honest about that. Three risks come up repeatedly in security research on MCP: a model being tricked into misusing a tool it has legitimate access to, sensitive data leaking through a server that wasn’t scoped narrowly enough, and authentication/permission handling getting complicated once many servers are connected to one agent. None of this is a reason to avoid MCP — it’s a reason to treat tool access the way you’d treat any other credential: scope each server to the minimum it actually needs, review what a given server can do before connecting it, and don’t hand an agent broad, unreviewed access just because the plumbing makes it easy to.

Common Misunderstandings About MCP

  • “MCP is an Anthropic-only thing.” It was Anthropic’s release, but it’s now used across OpenAI, Google, AWS, Microsoft, and IBM’s own tooling, and it’s governed independently through the Linux Foundation.
  • “MCP is a new AI model.” It’s not a model at all — it’s a connection standard that any model can use.
  • “MCP replaces APIs.” It standardizes how a model uses APIs and tools — the underlying APIs still do the actual work.
  • “You need to understand the full protocol spec to use it.” As a builder, you’re usually just installing or configuring an existing MCP server — the protocol details are handled underneath.

Frequently Asked Questions

Does MCP only work with Claude?
No. It was introduced by Anthropic, but OpenAI, Google, and other major providers have adopted it, and it’s now governed by an independent foundation rather than any single company.

Do I need to be a developer to benefit from MCP?
To build an MCP server, yes — it requires development work. To use an AI application that already has MCP servers connected (a growing number do), no coding is required; you’re just using tools that happen to be wired up this way underneath.

Key Takeaways

  • MCP turns the old problem of custom-building a connector for every AI-model-and-tool pair into a single shared standard — build or use a server once, connect it anywhere
  • It’s a connection layer, not a competing technology to APIs, function calling, or RAG — it standardizes how those pieces get used together
  • Adoption by OpenAI, Google, AWS, Microsoft, and IBM, followed by Anthropic handing governance to an independent Linux Foundation body, is a stronger signal of staying power than any single company’s announcement
  • The same standardization that makes tool access easy also makes over-broad tool access a real risk — scope what any connected server can actually do
  • You don’t need to understand the protocol internals to benefit from it; most builders are just connecting to servers that already exist

About the Author
Shurah writes and maintains DataPips independently, drawing on hands-on experience in trading and entrepreneurship. Articles are shaped by personal research, real trading lessons, and the process of building this publication from scratch — not by a formal financial credential.