AI ENGINEERING · UPDATED JUNE 2026

What Is the Model Control Protocol (MCP), and How Does It Actually Work?

Before MCP, connecting an AI agent to a tool or data source meant writing bespoke integration code for every single combination of model and tool. Every team solved the same problem slightly differently: how does the model know what tools exist, how does it call them safely, and how do you stop it from doing something it shouldn't. MCP, introduced by Anthropic and since adopted more broadly, standardises this into a client-server architecture that any compliant model or tool can plug into without custom glue code.

An MCP server exposes a set of capabilities — tools the model can call, resources it can read, and prompts it can use — over a standard protocol. The model, running inside an MCP client, discovers what's available by querying the server, then calls tools using a structured request format the server understands. This separation matters: the team building the MCP server doesn't need to know anything about which model will eventually call it, and the model doesn't need bespoke code for every tool it might use. The server simply describes what it offers; the protocol handles the rest.

In practice, building an MCP server starts with defining your tools clearly: a name, a description precise enough that a model can reliably decide when to use it, and a typed schema for its parameters. This is the same lesson that applies to function calling generally — vague tool descriptions produce unreliable tool selection, no matter how good the underlying model is. A tool called “query_database” with no further explanation will get called incorrectly far more often than one described as “retrieves customer order history by customer ID; use only when the user explicitly asks about a specific customer's past orders.”

One of the more useful ideas baked into MCP's design is per-tool scoping. Rather than an agent having blanket access to everything a server can do, each tool can carry its own authentication and permission requirements. A tool that reads public documentation might need no authentication at all; a tool that modifies customer records might require a specific scope that's only granted to certain agents or certain users. This matters enormously once you move from a demo to something handling real data, because the alternative — one big API key with access to everything — turns any prompt injection or model error into a much bigger problem than it needs to be.

A well-built MCP server typically also sandboxes anything resembling file system or code execution access, restricting it to a defined, isolated environment rather than the host machine directly. Read-only access is the default; write or destructive operations are deliberately made harder to reach, often requiring an explicit confirmation step or a narrower permission scope than read operations.

Because an agent's decisions about which tools to call aren't fully predictable, knowing exactly what happened after the fact is essential, not optional. A properly instrumented MCP server logs every tool call, the arguments passed, the result returned, and which agent or session triggered it. This isn't just good practice for debugging — in regulated environments, being able to show precisely what an AI system did and why is often a compliance requirement, and retrofitting that kind of audit trail after the fact is much harder than building it in from the start.

It's worth being clear-eyed about where MCP adds value versus where a simpler approach is fine. For a single internal tool used by one team, a direct function call is simpler and probably the right choice — MCP's overhead in setup isn't worth it for something that small. MCP earns its complexity when you have multiple tools, multiple consumers (different agents, different teams, possibly different models entirely), and a genuine need for consistent permissioning and audit logging across all of them. There's also been real, public debate about MCP's token overhead compared to direct API or CLI calls for narrow, high-frequency operations — for some token-efficient production pipelines, a direct call genuinely is more efficient, and MCP is not a universal hammer.

Building a first MCP server from scratch is a good way to understand the standard properly, and most of the complexity sits in getting tool descriptions, permission scoping, and logging right — not in the protocol mechanics themselves, which are deliberately simple. If you'd like a structured, hands-on path through this with an instructor, JBI Training runs a course specifically on it:

• Model Control Protocol  https://jbinternational.co.uk/course/3876/model-control-protocol-training-course
• Build Agentic AIs with Python, RAG and MCP  https://jbinternational.co.uk/course/4913/build-agentic-ais-with-python-rag-and-mcp-training-course
• View all GenAI - LLMs - Prompting courses  https://jbinternational.co.uk/courses/genai-llms

JBI Training delivers instructor-led AI and technology training to corporate teams across the UK and internationally, virtually and face-to-face.