In practice
It defines how a client and server describe available tools, make calls and return results. A common comparison is a port standard: build to the shared connection once instead of creating a different integration for every pairing.
MCP isn't a model, agent or product. It is a specification. The useful parts are the clients and servers built on it.
Say you use Claude Code, Cursor and a chat assistant, and you want all three to read the same list of customer requests. Without a shared protocol, someone builds three separate connections, one for each app. With MCP, the source is built once as a server and each app connects to it the same way.
Messages use JSON-RPC 2.0, carried over stdio for a program on your machine or Streamable HTTP for a server at a URL. The MCP server entry covers what local and remote mean for you.
The protocol is open and public. The specification, the SDKs for building with it and some reference servers are all published on modelcontextprotocol.io. Its authors took some inspiration from the Language Server Protocol, which did a similar job for programming languages in code editors.
MCP isn't an API, and it doesn't replace one. It also doesn't say how an app uses its model or manages the context it receives. The MCP documentation states that it focuses solely on the protocol for context exchange. It says nothing about whether an answer is any good.
How a call travels
- The AI app, called the host, creates one client for each server it connects to.
- The client asks the server what it offers, for example with a request to list its tools.
- The app gives that list to the model, along with your request.
- The model picks a tool and the arguments to use.
- The client sends the call to the server, ideally after you have approved it.
- The server runs it and returns a result, and the app hands the result back to the model to use in its answer.
The guide on MCP servers for product management shows what this looks like for product work.
Common mistakes
- Using MCP and MCP server as the same word. The protocol is the rules. A server is one program that follows them.
- Assuming every app supports every part of it. Clients differ in which features they implement, so check what yours supports.
- Assuming the protocol makes tools safe. The specification says it cannot enforce its own security principles at the protocol level, and leaves consent and access control to the apps built on it.
- Treating it as a replacement for APIs. Most servers wrap an API. The protocol sits above it.
- Forgetting that the spec still changes. Check which version your tools target before you rely on a detail.
Why it matters for product builders
The integration problem changed from every app connecting separately to every source, to clients and servers each supporting 1 shared protocol. That is a major reason agent tooling expanded so quickly through 2025.
**Source note**
Anthropic published the [Model Context Protocol](https://modelcontextprotocol.io) as an open standard in November 2024.
For a product builder, the protocol decides which of your tools your agents can reach. A product with an MCP server can be used from Claude Code, Cursor and others without a separate integration for each. A product without one stays outside the agent's working view, and you carry information across by hand.
It is also a buying signal. If a tool says it supports MCP, the useful follow-up is which tools it exposes and whether they read, write or both.
What changes in AI-first products
The protocol is new to the AI era. Anthropic announced it in November 2024, and it was built so that AI apps could reach outside systems without custom code for each pairing. What changed is the number of things an agent can reach in one session.
What didn't change are the old questions about any integration: who is allowed in, what they can touch, and who answers when something goes wrong. A shared standard makes connecting easy. It makes deciding what to connect no easier.
See how Annsa does it: Integrations →