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Build guide

To build an MCP server, decide what the assistant should be able to do, define each action as a tool with a clear name and inputs, implement the tools with an official SDK, choose a transport, add permissions, then test with a client before you deploy.

By HEU.AI ·

What does an MCP server expose?

An MCP server is a program that gives an AI assistant tools, data and prompts through the Model Context Protocol. Tools are actions the assistant can call. Resources are data it can read. Prompts are reusable templates.

How do you make an MCP server, step by step?

  1. List the actions and data the assistant needs, and write down what it must never do.
  2. Choose an SDK. Official SDKs exist for many languages, including TypeScript, Python, Java, Kotlin, C# and Go, so use the one you already work in.
  3. Define each tool with a short name, a description written for the model and typed inputs. Keep each tool narrow.
  4. Choose the transport: stdio for a local server, Streamable HTTP for a remote one.
  5. Add authentication and permissions. MCP defines an OAuth-based authorization flow for servers reached over HTTP.
  6. Test with an MCP client. Call each tool and check its inputs and outputs.
  7. Deploy, then log each tool call and review what is being called.

How do you write the code?

The SDKs let you register a tool with a name, a description and input fields, then connect a transport. Function signatures change between SDK versions, so follow the current docs for your language.

How do you keep an MCP server safe?

Each tool you expose is a way in, so give the assistant the least access it needs. Make tools read-only by default, set permissions tool by tool, and put a named person in front of any action with real consequence.

When should you get help?

If you would rather have an MCP server built for you, or the server must reach several internal systems, see our MCP server development services. Related: LLM integration.

FAQ

What is the difference between stdio and Streamable HTTP?

stdio runs the server on the same computer as the assistant. Streamable HTTP lets the assistant reach the server over a network, so it suits shared and remote use.

Do I need an MCP server if I already have an API?

Your API stays as it is. An MCP server sits in front of it and describes the tools in a standard way, so assistants can find and call them without custom code.

How does an MCP server work when an assistant uses it?

The assistant asks the server what tools exist. It then calls one with the inputs it needs, and the server runs it and returns the result.

Can one MCP server work with more than one AI assistant?

Yes. MCP is an open standard, so any assistant that supports it can connect to the same server.

Want an MCP server built for you?

Book a free consultation and we will look at the systems your assistant needs to reach and what each tool should be allowed to do.

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