Building a Custom MCP Server in 30 Minutes

When no ready connector exists for your system, you can build a custom MCP server that exposes your own tools to an AI agent in about half an hour — wrapping your internal API, database, or service in the Model Context Protocol. The structure is simple; the discipline is in marking writes and scoping auth.

What you're building

An MCP server is a small program that advertises a set of tools — named functions with typed inputs — and executes them when an agent calls. Your job is to define those tools over your existing system: a lookup_customer tool that queries your database, a create_request tool that writes a record, a check_status tool that hits your API. The server handles the protocol; you fill in the bodies with calls you've probably already written elsewhere. See what is an MCP server for the concept.

Start from a template

Don't write the protocol layer by hand. Start from an SDK template that handles the handshake, tool registration, and transport, so you only implement tool logic. Define each tool's name, a clear description (the agent reads this to decide when to use it), and a typed input schema. A good description and schema are what make the agent call the tool correctly, so spend your time there rather than on plumbing.

Mark your writes

This is the rule that matters most. Every tool that changes data — creates, updates, deletes, or reaches the outside world — must be marked as mutating. That flag is what lets AIML.chat wrap the tool in write-confirm, so a human approves before it runs. A read tool that's accidentally able to write, or a write tool not flagged as mutating, is a safety hole. Be deliberate: if in doubt, mark it mutating.

Scope the auth

Decide how the server authenticates and keep it minimal. Many servers need no auth for read-only public data; others use a bearer token or a custom header for a scoped API key. Whatever you choose, scope the credential to least privilege — a single dataset, a read-only role where possible — so the server's reach is bounded at the source. Never log secrets. See MCP authentication for the options.

Test before connecting

Before you attach the server to a live agent, test it. Verify the handshake completes, list the tools and check their schemas, confirm auth works, and make sure mutating tools are flagged. Catching an over-permissive tool or a broken schema at this stage is far cheaper than discovering it in a real conversation. A small test harness pays for itself.

Connect it

Once it runs and tests clean, register the server's URL in AIML.chat under the custom MCP option, add its auth, and attach its tools to an agent. Reads run automatically; writes wait for approval. You now have a custom capability — your own system — available to an agent, built in an afternoon and portable to any MCP platform. See the custom MCP integration.

Getting started

Scaffold and connect a server on the AIML.chat free plan. Compare tiers on the pricing page, and read why we chose MCP over custom tool APIs for the rationale.