MCP: Why Model Context Protocol Is Becoming an Important Part of AI
MCP went from an Anthropic side project to a Linux Foundation standard backed by OpenAI and Google in about a year. What it solves, how it works and where real integrations still fall short.
Every AI assistant eventually hits the same wall. The model can write, summarise and reason, but it can’t see your repository, your calendar or your company’s database unless someone builds a connection to each of them. Multiply that by every assistant on the market and every service people use, and you get a lot of duplicated glue code.
Model Context Protocol, or MCP, is the industry’s attempt to write that glue once. It’s worth understanding even if you never build an integration yourself, because it decides what your AI tools can actually reach.
From one company’s protocol to an industry standard
Anthropic announced MCP in November 2024 as an open standard for connecting AI assistants to data and tools. At first it looked like a Claude feature with an open-source label. Then competitors adopted it: OpenAI and Google DeepMind added support during 2025, and editors, IDEs and agent frameworks followed.
In December 2025 Anthropic handed MCP over to the Agentic AI Foundation under the Linux Foundation, co-founded with Block and OpenAI. That matters more than it sounds. A protocol owned by one AI vendor is a risk for everyone else, while a protocol governed by a neutral foundation is something companies are willing to build on.
The problem it solves
Before MCP, connecting AI to tools meant vendor-specific solutions, such as OpenAI’s function calling or the old ChatGPT plugins. If you wanted your service available in five AI apps, you built five integrations. Anthropic called this the N×M problem: N applications times M services.
MCP turns it into N+M. A service publishes one MCP server, an AI application implements one MCP client, and any client can talk to any server. The usual comparison is USB: you don’t need a different port for every device, as long as everyone agrees on the plug.
How it works
The architecture has three roles. The host is the AI application you use, for example a chat app, an IDE or a coding agent. Inside it, an MCP client connects to each MCP server, which exposes a specific system. Despite the name, a server can be a small local process on your laptop just as well as a remote service.
A server can offer three kinds of things:
- Tools are actions the model can call, such as searching issues, running a database query or creating a calendar event.
- Resources are data the model can read, such as files, documentation or records.
- Prompts are reusable instruction templates the application can offer to the user.
MCP doesn’t replace REST APIs. Most MCP servers simply wrap an existing API. What MCP adds is a standard way for the service to describe what it can do, in a form an AI model can discover and use without custom code in every client.
Where it falls short today
I use MCP with GitHub, GitLab, Trello, Gmail, Google Drive and the Czech online grocery Rohlík.cz. The idea works, and asking an assistant to “find the open bugs, read the related docs and draft a fix” is genuinely different from copying everything into a chat by hand.
The implementations are another story. Many official MCP servers expose only a small slice of the underlying service. The GitHub integration I use can’t handle binary files, the Trello one can’t work with comments, and through the grocery integration I couldn’t even get a banana into the cart. Too many servers today feel like a demo rather than something you can rely on for daily work.
This will probably improve as MCP becomes something customers expect, the same way public APIs went from afterthought to product feature. For now it’s worth checking what a specific server can really do before building a workflow around it.
Security: permissions and prompt injection
Once a model can act, permissions stop being a detail. Letting an assistant read documentation is very different from letting it push to a repository, send email or change data. I treat MCP servers like apps asking for permissions on a phone: grant what the task needs, not everything that’s technically possible.
There’s also a newer risk. A model that reads external content, such as an issue, an email or a web page, can be manipulated by instructions hidden in that content. This is called prompt injection, and MCP makes it more relevant, because a manipulated model now has tools to act with. Read-only access, confirmation before destructive actions and servers from trusted sources reduce that risk considerably.
Why it matters
The interesting change in AI right now isn’t another point on a benchmark. It’s the move from models that answer questions to agents that work with the systems people actually use, and that move needs a common way to connect.
MCP doesn’t make agents smart or integrations complete. What it does is remove the need to rebuild the same connection for every combination of app and service, and with neutral governance and support from all major AI vendors, it looks like the plug the industry has settled on.