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AI Agents & MCP

What building an MCP server for your business involves

A practical walkthrough of scoping, building and running an MCP server: which tools to expose, what to withhold, and what it costs each month.

4 min read
What building an MCP server for your business involves. An article by Athabasca Solutions.

Once a team is using an AI assistant every day, the obvious next question is whether it can reach the systems the business actually runs on. That is what an MCP server does, and what MCP is covers the concept.

This is the other half: what building one actually involves, in order, and what it costs to keep running.

Step one is not technical

Before any code, collect the questions your team asks that a computer could answer. Real questions, taken from real messages, not invented ones.

You are looking for the shape of the list. Typically it comes back like this:

  • Five or six questions asked constantly, all read-only, all answerable from one system.
  • A long tail asked once each, which nobody should build for.
  • Two or three actions people want taken, which are where all the risk lives.

The first group is the project. Build for those, ship, and let usage tell you whether anything from the tail deserves promoting.

Choosing what to expose

An MCP server exposes tools, and the instinct is to expose the underlying API one to one. Resist it. An API endpoint is designed for a programmer who has read the documentation. A tool is designed for an assistant working from a description.

The difference in practice:

  • Name tools after the question, not the endpoint. find_overdue_jobs rather than query_jobs with fourteen optional parameters.
  • Fewer parameters, sensible defaults. Every parameter is a chance for the assistant to guess wrong.
  • Return prose alongside data. A tool that returns raw JSON forces the model to interpret. A tool that returns a short summary plus the data gets used correctly more often.
  • Return less. Truncate long result sets and say so. Handing back four hundred records wastes the context window and degrades the answer.

Ten well-shaped tools beat fifty faithful API mirrors, consistently.

The security model, which is the actual work

An MCP server is a new path into your systems, and it needs to be designed as one rather than bolted on.

Its own credentials. Never a shared admin account. Scope them to exactly the tables or endpoints the tools need, and no more.

Read and write separated deliberately. Start read-only. It covers most of the value and it cannot cause an incident. Add writes one at a time, each with a reason.

Approval gates on anything outward-facing or destructive. Sending an email, issuing a refund, deleting a record. The tool should stage the action and require a human confirmation, not perform it and report back.

Every call logged, with who asked. When somebody asks what the assistant did on Tuesday, you want a record, not a reconstruction.

Untrusted content treated as untrusted. This is the one people miss. If a tool returns a customer’s message, and that message contains instructions, the model may follow them. Data returned from your systems is data, and the server should be built so that content read from a record can never be mistaken for an instruction from the operator.

That last point is the reason to keep the tool surface narrow. A server that can only read job statuses has a small blast radius no matter what anyone puts in a job description field.

Effort and timeline

For a system with a working API and clear questions, a focused read-only server is a small project, generally one to three weeks including the scoping conversation.

It grows when:

  • The underlying system has no usable API, in which case that is the project and MCP is the last week of it.
  • Data needs cleaning before the answers are trustworthy. An assistant reporting confidently from bad data is worse than no assistant.
  • Write access is required, because approval flows, idempotency and audit are each real work.

What it costs to run

Very little, which surprises people. An MCP server only does work when somebody asks a question, so it fits a serverless function well. A small team’s usage lands in single-digit dollars a month, plus whatever the assistant subscription already costs.

The real ongoing cost is maintenance. When the underlying system changes, the tools change, and somebody has to notice. Budget for that attention rather than for compute.

Hosting a server that is genuinely public, rather than internal, is a slightly different question, mostly around rate limiting and abuse, and it is worth handling deliberately if you go that way. Our cloud and hosting work covers that side.

How to start small and be sure

Pick the single question your team asks most. Build one read-only tool that answers it. Give it to three people for two weeks.

You will learn three things quickly: whether the answers are right, whether people actually ask the assistant instead of each other, and which tool they wish existed next. Those three answers are worth more than any amount of up-front design, and they cost a week.

If you want a straight read on whether MCP fits your systems, tell us what your team keeps asking and what runs the business.

Related: AI agents that do real work, the guardrails an agent needs, and our AI agents and MCP work.

Further reading

Sections covered in What building an MCP server for your business involves: Step one is not technical, Choosing what to expose, The security model, which is the actual work, Effort and timeline, What it costs to run, How to start small and be sure
The shape of the argument, in order.

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