AI · · 3 min read
MCP servers for agents: what they are and when to build one
Model Context Protocol is redefining how agents access tools and data. Here's when it's the right choice — and when it isn't.
What MCP is
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in November 2024 and since adopted by many AI vendors and tools, for connecting AI applications to external systems. An MCP server exposes capabilities; an MCP client — a chat app, an IDE assistant or an agent framework — discovers and uses them.
A server can offer three kinds of things: tools (actions the model can call, such as “search tickets” or “create invoice”), resources (data the application can read, such as files or records) and prompts (reusable templates). Servers run locally over standard input and output, or remotely over HTTP.
Why it matters
Before MCP, every AI application needed its own integration for every system. With MCP, you describe your system once and any compatible client can use it. For a product company, that means customers can connect your product to the AI tools they already use. For an internal team, it means one integration serves every assistant and agent in the company.
When to build an MCP server
- You want customers to use your product from AI assistants and agents they already have.
- Several internal agents or assistants need the same tools and data.
- You want one well-governed gateway to internal systems, with consistent authentication and logging.
- Your team already maintains an API, and an MCP layer on top is a thin addition.
When not to
- You are building one AI feature inside one application with one model — direct function calling is simpler.
- The actions are high-risk (payments, deleting data) and there is no plan for permissions and human approval.
- Your underlying API is not stable yet; fix that first.
Designing good tools
Models choose tools from their names, descriptions and input schemas, so write them for a reader who knows nothing about your system. Prefer a few focused tools (“find_customer_by_email”, “list_open_tickets”) over one tool with many modes. Return compact, structured results with IDs the model can use in the next call, and helpful error messages when input is wrong.
Separate reading from writing. Read-only tools can often run freely; tools that change data should require the right scope and, for important actions, a confirmation step in the client.
Security
Remote servers should use proper authorisation — the MCP specification builds on OAuth — and each token should carry only the permissions the user actually has. Log every tool call with who made it and what changed. Most importantly, remember that tool results end up in the model’s context: a ticket, email or web page returned by a tool can contain text that tries to give the model new instructions. Treat tool output as data, limit what one session can do, and keep humans in the loop for anything destructive.
Getting started
Official SDKs exist for TypeScript, Python and several other languages, and community packages cover PHP and Laravel. Start with two or three read-only tools over an existing API, test them with the MCP Inspector and a real client, watch how the model uses them, then refine descriptions before adding write actions. The quality of an MCP server is decided less by the protocol than by clear tools and careful permissions.
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