The Model Context Protocol (MCP) is an open standard that lets AI assistants connect to your tools and data through one common interface. You build the connection once, as an MCP server, and any MCP-compatible AI can use it. In plain terms, it is what turns a model that can only talk into an assistant that can actually do things in your systems: read your data, call your tools, and take real actions.
People often call it “a USB-C port for AI.” One standard plug, and anything that speaks it can connect. Here is what that means in practice and when it matters.
What problem does MCP solve?
Before MCP, every connection between an AI model and a system was bespoke. If you had M different AI apps and N different systems you wanted them to reach, you faced building close to M times N custom integrations, and re-doing the work every time a model or a tool changed.
MCP collapses that to M plus N. Each system gets one MCP server. Each AI app is an MCP client. Any client can talk to any server. Build the connection to your CRM once, and Claude, Copilot, or your own in-house agent can all use it without new glue code.
That is the whole point: write the integration once, reuse it everywhere.
How MCP works, briefly
MCP has two sides:
- An MCP server wraps a system (a database, a SaaS tool, a file store, an internal API) and exposes what the model is allowed to use.
- An MCP client, inside the AI app, connects to those servers and lets the model discover and call what they offer.
A server exposes three kinds of thing:
- Tools: actions the model can take, like “create a ticket” or “query sales by region.”
- Resources: data and context the model can read, like documents or records.
- Prompts: reusable templates for common tasks.
The model discovers these at runtime, so you can add a new tool to a server and the assistant can use it without retraining or rebuilding the app.
What you can actually do with it
A few concrete examples of MCP in a business:
- An assistant that answers questions from your internal documents and database, instead of guessing (usually built with RAG).
- A support agent that reads a ticket, looks up the customer in your CRM, and drafts a reply grounded in their actual history.
- An analyst assistant that queries your data warehouse in plain English and returns real numbers.
- A coding assistant that can read your repo, run tests, and open a pull request.
In each case MCP is the layer that lets the model reach the real system safely, rather than being a clever chatbot stuck behind glass.
MCP vs an API vs function calling
These get muddled, so here is the distinction:
| What it is | Who it is for | |
|---|---|---|
| API | How any two pieces of software talk | Developers, generally |
| Function calling | A model emitting a structured request to run one function | A single app’s own tools |
| MCP | A standard for how models discover and use tools and data at runtime | Any model, any system, reused |
Function calling lets one app give its model some tools. MCP standardises that so the same tools work across different models and apps, and an MCP server usually just wraps the APIs you already have. They are layers, not rivals.
When would you actually use MCP?
Reach for MCP when:
- You want an AI assistant to use more than one system, or you expect the list of systems to grow.
- You want to avoid lock-in to a single AI vendor, so today’s Claude tool also works with tomorrow’s model.
- Different teams or agents need to reuse the same connection to a system.
If you only need a model to call one function inside one app, plain function calling is enough. MCP earns its place when reuse and breadth are in play. This is the same judgement as deciding whether you need an agent or just automation: match the tool to the actual shape of the problem.
A word on security
MCP grants a model access to real systems, so the guardrails matter as much as the capability. A well-built MCP server exposes only the tools and data the model genuinely needs, runs with least-privilege credentials, logs what it does, and keeps a human in the loop for anything sensitive or irreversible. The protocol opens the door; deciding what is behind it is a design decision, and the difference between a useful assistant and a liability.
The short version
MCP is the common connector between AI models and your systems. It exists so you build each integration once and reuse it across any compatible AI, which is what makes an assistant genuinely useful rather than a demo. If you are connecting AI to the tools your business already runs, it is usually the right foundation to build on.
We build AI agents and automation and custom software on exactly this footing: real access to your systems, with the guardrails to trust it. If you want to work out where MCP fits for you, talk to us.
Frequently asked questions
What is the Model Context Protocol?
MCP is an open standard that lets AI assistants connect to external tools, data, and systems through one common interface. You build the connection once as an MCP server, and any MCP-compatible AI, such as Claude or Microsoft Copilot, can use it.
What is MCP used for?
Giving an AI model safe, structured access to the things it needs to do real work: your files, databases, SaaS tools, and internal systems. It turns a model that can only generate text into one that can fetch real data and take actions in your systems.
How is MCP different from a normal API?
An API is how two pieces of software talk in general. MCP is a specific standard for how an AI model discovers and uses tools and data at runtime. Under the hood an MCP server usually just wraps your existing APIs; MCP is the common layer that lets a model use them without bespoke glue for each one.
Who created MCP and is it open?
Anthropic introduced MCP and open-sourced it in late 2024. It is an open standard with a public specification and SDKs, and the major AI vendors have since added support, so it is not tied to one provider.
Is MCP secure?
It can be, if built properly. An MCP server should expose only the tools and data the model genuinely needs, run with least-privilege credentials, and keep a human in the loop for sensitive actions. The protocol grants access; the guardrails are your responsibility.
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