The Model Context Protocol (MCP for short) is an open standard through which AI applications reach external tools and data sources. Instead of writing a bespoke connection for every combination of AI application and data source, both sides speak the same protocol: a server advertises what it has to offer, a client uses it. Anthropic published the protocol at the end of 2024 and made it open, documented and free for anyone to implement.
The comparison that holds up best: MCP wants to be for tool access what a standard connector is for peripherals. Not spectacular, but it saves a great many adapters.
The problem the standard solves
A language model on its own knows nothing about your company. It only becomes useful once it can reach your data: the ticket system, the document store, the database, the CRM. That is exactly what connections are written for.
Without a standard, this effort grows as a product rather than a sum. With five AI applications and ten systems you have fifty connections, each with its own authentication, its own data format and its own maintenance. That is the classic N-times-M problem, and it is why integration projects rarely fail because of the model.
| Without a protocol | With MCP | |
|---|---|---|
| Number of connections | one per application-and-system pair | one per application, one per system |
| Reuse | none, every connection is bespoke | one server serves every client |
| Switching AI application | rewrite every connection | servers stay, the client is swapped |
| Maintenance | spread across many projects | pooled in one place per system |
So the benefit is arithmetic rather than technical magic. Fifty one-offs become fifteen reusable pieces.
Client and server
MCP works with a clear split of roles that is worth getting straight once, because the terms sit the opposite way round to how people often expect.
The server
The MCP server sits at the data source or the tool. It wraps one system, a database say, a file directory or an interface, and describes outwards what it can do: which tools it offers, what inputs they need, which data it makes readable. It holds all the logic for that one system, including authentication and error handling.
One point to keep straight: an MCP server is not necessarily a service somewhere on the network. It can just as well be a program started locally on the same machine. With development tools that is in fact the normal case.
The client
The client sits inside the AI application, meaning the program people actually work in: a chat window, a development environment, an AI agent. It connects to one or several servers, asks what they offer, and presents that to the model as available tools. When the model decides to use a tool, the client sends the call to the right server and returns the result into the context.
The model itself does not speak MCP. It only sees a list of tools and their descriptions. The whole standard lives one layer below, in the application.
What a server can offer
Broadly, what a server exposes falls into three kinds. The distinction is practical and mostly describes who triggers the use.
- Tools. Callable functions with an effect or a return value: a search, a database lookup, creating a ticket. The decision to use them is made by the model.
- Resources. Readable content that serves as context: files, records, documentation. They are read, not executed.
- Prompts. Prepared building blocks for recurring tasks that an application can offer so people can pick one.
For the exact details, the official specification is the place to look, because the protocol keeps evolving and not every client application supports every part of it.
What it changes in practice
For a company, a standard mainly shifts one decision: the connection to your own system is no longer tied to a particular AI application. Build an MCP server for your inventory system today and you can use it with a different application tomorrow, without writing the integration again.
That is the good news. The sober half: a protocol solves none of the things that actually make integration projects expensive. Permissions, data quality, availability and the question of who maintains the connection all stay exactly where they were. MCP makes sure you do that work once instead of five times.
There is also a security consideration of its own. A server an AI application may call is an entrance to a system, and whatever it returns lands in the model’s context. That makes it potentially instructions, see prompt injection. A dedicated account with a narrow set of rights is not over-caution here, it is the normal case.
Frequently asked questions about the Model Context Protocol
Is MCP tied to a particular model? No. The protocol describes the connection between application and data source, not the model behind it. Which large language model the application uses is invisible to the server.
Do I need MCP if I am building my own application anyway? Not necessarily. For exactly one application and exactly one data source, a direct connection is quicker to build. The standard pays off once several applications need the same source, or once you want to keep the option of swapping the application.
How does it differ from an ordinary interface? MCP does not replace an interface, it sits on top of one. Internally a server still uses the system’s REST or database interface. What it adds is a uniform description of what a model is allowed to do here and how to read what comes back.
Are there ready-made servers, or does everything have to be built? Implementations already exist for many common systems, some from the vendors, some from the community. For in-house systems you write the server yourself, which stays manageable thanks to the open specification and libraries in several languages.
Conclusion
The Model Context Protocol is an unshowy decision with real consequences. It turns connecting AI applications into something you solve once per system rather than afresh per project, and it decouples your own integration from the question of which AI application happens to be ahead. The hard part of an integration project remains, it simply stops being paid for repeatedly. How we build interfaces, permission models and system connections is described on our page about custom software development. Our assessment of what using AI in development actually delivers is in the article AI in software development. What these connections are ultimately needed for, namely systems that carry out steps themselves, is on our page about AI agents, and for anything beyond that there is a free consultation.