Run your first server

Start with the coffee example. It answers questions about the origin, roast, processing method, and tasting notes of two made-up coffees. It includes both ordinary tests and tests of AI tool choice and explanations.

You need Git, Node.js 22 or 24, and npm. This is a local development quickstart, not production deployment guidance.

Copy the example

Run these commands from a directory where you keep projects. Choose another name if my-mcp or emseepea already exists.

Copy the example

git clone https://github.com/emseepea/emseepea.git
cp -R emseepea/examples/basic-no-ui my-mcp
cd my-mcp

Your project is now separate from the monorepo. Its package.json pins exact pre-alpha versions of @emseepea/server and @emseepea/testing.

Install and check it

Run these commands inside my-mcp:

Install and check

npm install --ignore-scripts
npm test
npm run lint

The tests build your server and call it through MCP. They do not require a language-model account. A passing run ends with no failed tests.

Start your server

Start the server

npm start

The terminal prints its local MCP address, normally http://127.0.0.1:3000/mcp. Keep that terminal open while using the server. Stop it with Control-C. If port 3000 is in use, stop the other local example first.

Connect using an MCP client that supports MCP 2026-07-28 and Streamable HTTP. Add the printed address to that client’s server connections, then try asking:

What are the origin, processing method, roast, and tasting notes of Highland Bloom?

Client setup varies. Older clients may not support this protocol version. The example’s tests use the matching official MCP client.

Make it yours

Open src/server.ts in your project:

• beans contains the sample data. Replace it with your own data.
• inputSchema describes the names the tool accepts.
• outputSchema describes the details it returns.
• handler looks up the coffee and returns text plus structured data.

Em See Pea checks the input and output. Your handler supplies the behaviour. Update test/server.test.mjs for your data, then run npm test again.

Check the AI’s tool choice and explanation

The example’s eval/meaning.test.mjs checks that a model does not confuse origin, variety, processing, and roast. Adapt its question and expected facts when you change the tool.

This check requires a signed-in Claude CLI and uses model allowance. See the AI testing guide for setup and what the results prove.

Check AI tool choice and understanding

npm run test:llm

The model sees the advertised tools and must select get-bean-details with arguments that the real server accepts. The test then checks whether the model understands the returned coffee details.

Add another capability

Browse the examples to connect a public API, add sign-in, send progress updates, or add a web form.