MCP integrations: connecting your product's data and tools to AI agents

Before you build anything

Read what MCP actually is first if you have not, because the integration decisions below only make sense once the client-server model is clear. Before writing a line of server code, answer three questions: which system is the agent connecting to, what is the smallest set of actions that accomplishes the actual job, and what happens if the agent calls the wrong tool at the wrong time. That last question decides your permission model.

Choosing what to expose

The instinct is to expose an entire API's surface through one MCP server because it is already there. Resist it. Every resource or tool you expose is something an agent might call incorrectly, and something a future audit has to account for. Start with the two or three operations the actual workflow needs, ship that, and expand deliberately as new jobs justify it.

Designing the server boundary

One MCP server per system, or per bounded capability, is usually the right grain, a support-ticketing server, a billing-read server, an internal-docs-search server, rather than one monolithic "company API" server that hands an agent everything at once. This maps cleanly onto multi-agent architectures, where different agents connect to different servers based on their role, and it makes each server small enough to actually test thoroughly.

Permissions and scoping

• Read before write. Ship read-only resources first; add write actions only once the read path is trusted and monitored.
• Scope by identity, not by server. The server should enforce what a given user or agent session is allowed to touch, not assume every caller gets full access.
• Log every call. An agent's tool calls are the audit trail you will need the first time something goes wrong. Treat MCP server logs as seriously as production API logs, because that is what they are.
• Rate-limit and cap cost. An agent in a retry loop can hammer a tool far faster than a human ever would.

An MCP server is a production API with an unusually literal-minded client. Every input-validation habit you already have still applies.

A practical build sequence

In order: define the job and the narrowest tool set for it; build the server against a staging version of the underlying system, never production first; write integration tests for each exposed tool independently of any agent; connect a single agent and test the full loop against realistic prompts; then only after that, expand scope or add a second consuming agent. This mirrors standard API and contract testing discipline, because an MCP server is, underneath the protocol, an API.

Common integration mistakes

The failures we see most often: exposing a destructive action (delete, refund, send) without a confirmation step or human-in-the-loop gate; trusting the model's own judgment as the only safeguard against calling the wrong tool; skipping load testing, since an agentic loop can call a tool far more often than a human workflow would; and never testing what happens when the underlying system is slow or down, which an agent can handle badly if you have not planned for it. Full coverage of how we test for these is in testing AI agents and MCP servers.

Scoping an MCP integration for your product and want it built with production discipline from day one? Talk to us, the first conversation is free.