What does the Model Context Protocol actually do?
MCP describes how an AI assistant and a software product talk to each other. It was released by Anthropic as an open source specification, and any vendor can implement it. The protocol has two sides. An MCP server sits in front of a product and publishes what it offers. An MCP client lives inside an AI assistant, a coding tool or an agent, and calls those offerings on the user's behalf.
A server exposes three kinds of things. Tools are actions the assistant can run, such as creating a draft or fetching a record. Resources are pieces of data it can read, such as a changelog feed or a list of customers. Prompts are reusable instructions the product can hand to the assistant. The assistant discovers all of this at runtime, so nothing has to be hard coded on the client side.
The point of a shared standard is reuse. Before MCP, every AI product needed a custom integration for every tool it wanted to reach. With MCP, a product builds one server and every compatible client can use it. The same server works whether the user sits in a chat window, an IDE or an automated workflow.
Why does MCP matter for SaaS product teams?
Customers increasingly ask their AI assistant to do work across the tools they already pay for. If your product is not reachable, the assistant cannot help with it, and the user goes elsewhere for the task. An MCP server turns your product into something an assistant can act on, not just talk about.
For product managers, this changes what an integration is. A changelog automation that once required a webhook and a script can now be a plain instruction to an assistant. Support and success teams gain the same reach. A customer success lead can ask for the latest release notes, the open feature requests from an account, and a draft reply, in one conversation.
There is also a positioning effect. Buyers evaluating tools now check whether a product has an official MCP server the way they once checked for a public API. Shipping one signals that the product is built to work inside agent driven workflows, which matters for teams that read integration depth as a sign of maturity.
What are examples of MCP in practice?
The pattern is easiest to see through common server types that exist publicly today.
- Code hosting. A server for a git platform lets an assistant list open pull requests, read a diff and open an issue without leaving the chat.
- Databases. A read only database server lets an analyst ask questions in plain language, and the assistant writes and runs the query through the protocol.
- Team chat. A messaging server lets an assistant read a channel thread and post a summary to another channel.
- Product communication. A changelog tool's server lets an assistant pull merged pull requests, draft a product update and publish it to the right segment. The release notes automation that used to need several tools becomes one request.
In each case the product did not build a chatbot. It described its capabilities once, and the assistants the customer already uses picked them up.
How should a SaaS team approach MCP?
- Start with the jobs customers already ask about. Publishing an update, checking status and searching records are better first tools than every endpoint in your API.
- Name tools for humans, not for code. An assistant chooses a tool by reading its name and description. "Create a draft post" beats "POST /v2/posts".
- Separate reading from writing. Keep read tools broad and write tools narrow, and require confirmation before anything is published or deleted.
- Respect existing permissions. The server should act as the signed in user, with the same roles and project scope they have in the product.
- Document it where people look. Treat the server like any other integration, with setup steps, example prompts and a changelog of its own.
- Announce it. An MCP server is a feature announcement worth a post, an email and an in-app message, because most users will not discover it by accident.
MCP vs API vs plugin: what is the difference?
An API is built for developers. It returns structured data and expects the caller to know the endpoints in advance. MCP sits on top of an API and adds a layer that an AI assistant can discover and reason about at runtime. Most MCP servers are thin wrappers over an existing API.
A plugin is tied to one host, so a plugin written for one assistant does not run in another. An MCP server is host neutral. Build it once and any compatible client can connect.
Common mistakes include exposing every endpoint at once, which confuses the assistant and raises risk, and skipping authentication on the assumption that the assistant will behave. Another is treating MCP as a replacement for a public roadmap or docs. It complements them. Teams that already publish good AI assisted release notes will find MCP the natural next step, because the content and the permissions are already in place.
How AnnounceKit handles model Context Protocol (MCP)
AnnounceKit ships an official MCP server so AI agents can work with your product communication directly. An assistant connected to it can draft posts with AI post generation, publish to your changelog page on your own domain, and target a segment instead of everyone. The same server reaches feature requests with voting and Jira sync, NPS surveys, email digests and Slack delivery.
Updates published this way appear in more than ten in-app widget display modes, so nothing changes for your end users. Pricing is flat per project from $79 per month, and every plan starts with a 15-day free trial.