How to build your own MCP server, step by step
To build an MCP server, decide which actions and data to expose, pick an official SDK (Python, TypeScript, Go or C#), define each tool with a clear name, description and input schema, implement the handlers that call your system, then test with MCP Inspector and a real client such as Claude. For shared use, deploy it as a remote HTTPS server with OAuth, minimal permissions and logging.
When should you build an MCP server?
Build one when an important system has no official server, when you need tighter control than a third-party option allows, or when you want to give AI assistants safe access to internal data. An MCP server is usually a thin layer over an existing API or database, so if you have built a small web service before, the work will feel familiar. If a good official server exists, use it instead; see our list of the best MCP servers.
How do you plan the tools?
- List real questions: Write down what people will ask the AI, such as “which orders for Acme are delayed?”.
- Map questions to actions: Each question becomes a tool or a resource, for example search_orders or get_order.
- Keep scope narrow: Start with read-only tools; add one write action only if it clearly helps.
- Define inputs: Specify each parameter, its type and allowed values.
- Decide approvals: Mark which tools change data and should require user confirmation.
Which SDK should you use?
| SDK | Good for |
|---|---|
| TypeScript | Web teams and Node.js services |
| Python | Data, AI and automation teams |
| C# | .NET and Microsoft environments; v2 is stateless by default |
| Go | High-performance services and infrastructure teams |
Official SDKs handle the protocol details, including the stateless 2026-07-28 revision. See what the stateless MCP spec changed before you start, so you build on the current model.
How do you build it?
- Create a project with the SDK: Install the official package and scaffold a server.
- Register your tools: Give each a name, description and input schema.
- Implement handlers: Write the code that calls your API or database and returns results.
- Add resources or prompts if useful: Expose read-only documents or reusable templates.
- Handle errors clearly: Return helpful messages the model can act on, not raw stack traces.
- Run locally: Start with a local transport while developing.
Why do tool descriptions matter so much?
The model never sees your code; it decides what to do from each tool’s name, description and schema. A clear description says what the tool does, when to use it, what it returns and what it must not be used for. Keep descriptions short and factual. Because descriptions steer the model, they are also an attack surface: never load them from untrusted sources, and review changes like code. Our guide to MCP security risks explains tool poisoning.
How do you test it?
- Use MCP Inspector to list tools and call them with test inputs
- Connect it to Claude or another client and ask realistic questions
- Check that the model picks the right tool and passes sensible arguments
- Try awkward cases: missing data, ambiguous requests, permission errors
- Confirm destructive tools trigger approval prompts
How do you deploy it for a team?
- Serve it over HTTPS as a remote server
- Add OAuth so each user signs in with their own account
- Give the server only the permissions its tools need
- Log every tool call with user, inputs and outcome
- Monitor errors and usage, and version your releases
What does a first server look like?
Take an order-support server for an e-commerce team. It exposes three tools: search_orders, which takes a customer name and optional status; get_order, which takes an order ID and returns items, delivery status and payment state; and flag_order, which adds a note for the support team and requires confirmation. Each handler calls the existing order API with a service account that can read orders and add notes, but cannot refund or cancel. Support staff can then ask Claude “which of Acme’s orders are late, and why?” and get a short, accurate answer. Refunds stay in the existing back office, with a person in control.
How do you maintain it?
- Version the server and keep a changelog
- Review any change to tool names or descriptions like a code change
- Upgrade SDKs when the specification changes
- Watch logs for tools the model misuses and improve descriptions
- Remove tools nobody uses
Start with a small, read-only server, put it in front of a few real users and improve it from what the logs show.
How UIUX Studio helps
Our product engineering team designs, builds and hosts MCP servers for internal systems, including authorisation, logging and client testing.
Frequently asked questions
How long does it take to build an MCP server?
A simple read-only server can be built in days; production hardening takes longer.
Which language should I use?
Use the SDK that matches your team: TypeScript, Python, C# or Go.
Do I need OAuth?
For shared remote servers, yes, so each user signs in with their own permissions.
Can I list my server in Claude’s directory?
Team and Enterprise organisations can submit servers for review.
How do I test it?
With MCP Inspector first, then in real clients such as Claude.