AI, explained
What is the Model Context Protocol (MCP)?
MCP is the USB-C port of AI integrations: one standard connector between an assistant and everything it needs to read or act on.
Before MCP, connecting an AI model to your business systems meant writing glue code twice over: once for each system you wanted to reach, and again for each model or product you wanted to reach it from. Ten systems and three assistants meant thirty integrations to build and maintain. MCP collapses that into ten. You describe a system once, in a standard shape, and any client that speaks the protocol can discover and use it.
The three things an MCP server exposes
- Tools. Actions the assistant can invoke, such as creating a ticket, querying a database, or sending a draft for approval. Each tool declares its inputs so the model knows how to call it correctly.
- Resources. Read-only context the assistant can pull in, such as a document, a table, or a file tree.
- Prompts. Reusable, parameterised instructions the server publishes so common workflows are executed the same way every time.
Why it matters commercially, not just technically
The practical value of MCP is optionality. Integration work is the expensive part of any AI project, and historically it was the part you had to redo whenever you changed model providers or assistant products. Building against a standard means the integration outlives the model choice. If a cheaper or stronger model appears in six months, you swap the model, not the plumbing. That is a genuine reduction in lock-in, and it is the reason MCP moved from a single vendor's proposal to something implemented across the industry within a year.
Where teams get it wrong
Two mistakes recur. The first is exposing too much: giving an assistant a tool that can delete production records because it was easy to wire up. Scope every tool to the narrowest useful permission, and keep destructive actions behind a human approval step. The second is treating tool descriptions as an afterthought. The model chooses tools by reading their descriptions, so a vague description produces a confused agent. Write them as you would write documentation for a new colleague.
How this shows up in real deployments
In production work, MCP is usually the layer between an agent and the systems a business already runs: the CRM, the ticketing system, the document store, the internal database. The agent stops being a chat window with general knowledge and starts being something that can actually see your data and do your work, with an audit trail of every call it made. If you are weighing whether that is worth building, an AI audit is the fastest way to find which of your systems would repay the connection first.
Frequently asked questions
What is the Model Context Protocol in simple terms?
It is a shared standard for connecting AI assistants to outside tools and data. You expose a system once as an MCP server, declaring the actions and information it offers, and any assistant that speaks the protocol can then use it. That replaces the old pattern of writing a separate custom integration for every combination of model and system.
Is MCP only for Anthropic models?
No. Anthropic created and open-sourced the specification, but it is not tied to one provider. Adoption spread quickly across AI products, developer tools, and platform vendors, which is the point of a standard. An MCP server you build today can be consumed by clients from multiple vendors without changes.
Do I need MCP to build an AI agent?
Not strictly. You can call tools directly through a model provider API. MCP becomes worth it when you have several systems to connect, several assistants that need them, or a desire to avoid rewriting integrations when you change models. For a single narrow integration, direct tool calls are often simpler.
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