Simple Sampling

👨‍💼 Our users love the prompt functionality, but they asked why the LLM couldn't just suggest tags when they create the post and we thought that's a great idea!

So now your goal is to make your server request a simple completion from the language model whenever a new journal entry is created.

In this first step, we'll just get things wired up and then we'll work on our prompt for the LLM in the next step.

Here's what you'll do:

Implement a function that sends a sampling request to the client using agent.server.server.createMessage (the server.server thing is funny, but our MCP server manages an internal server and that's what we're accessing).
Use a simple system prompt (e.g., "You are a helpful assistant.") and a user message that references the new journal entry's ID (we'll enhance this next).
Set a reasonable maxTokens value for the response.
Parse the model's response using a provided Zod schema.
Log the result to the console so you can inspect the model's output.

And don't forget to call it when the user creates a new journal entry!

The maxTokens option references the max tokens the model should return.

The system prompt + messages + OUTPUT MESSAGE can't exceed the context window size of the model you're user is using.

This step will help you get comfortable with the basic request/response flow for sampling in MCP, and set the stage for more advanced prompt engineering in the next step.

05. Sampling/01. Simple Sampling (💪 problem)

Simple Sampling