Prompts

Sometimes there are common workflows for people using your MCP server you want to make easier for users. You may not want your users to have to write the same prompt all the time for that workflow (not everyone is a "prompt engineer").
The Model Context Protocol (MCP) has a specification for prompts as a way for servers to expose reusable, structured instructions to language models and clients. Prompts are more than just static text—they can be parameterized and invoked by users to guide model behavior in a consistent, transparent way.
With prompts, servers can offer a menu of available instructions (like "summarize my journal entries from last week," "write alt text for this image" or "review this code"), each with a clear description and customizable arguments. This enables richer, more user-driven interactions, where clients can select and fill in prompts as needed.
From the MCP Spec: Prompts are designed to be user-controlled, meaning they are exposed from servers to clients with the intention of the user being able to explicitly select them for use. Typically, prompts would be triggered through user-initiated commands in the user interface, which allows users to naturally discover and invoke available prompts. However, implementors are free to expose prompts through any interface pattern that suits their needs—the protocol itself does not mandate any specific user interaction model.
Here's an example of how the user experience might look:
ServerClientLLMAppUserServerClientLLMAppUserOpen prompt menuRequest available promptsList promptsPrompts listPrompts listShow promptsSelect prompt & provide argumentsGet prompt templateGet prompt templatePrompt templatePrompt templateFill prompt & invokePrompt messages (structured)Show LLM response
Again, the only part here that's specified is the client and server communication. The rest is up to whoever implements the client.
For example, a server might expose a simple "hello world" prompt:
import { z } from 'zod'

agent.server.registerPrompt(
	'hello_world',
	{
		title: 'Say hello to the user',
		description: 'Say hello to the user',
		argsSchema: {
			name: z.string().describe('The name of the user to say hello to'),
		},
	},
	async ({ name }) => ({
		messages: [
			{
				role: 'user',
				content: { type: 'text', text: `Hello, ${name}!` },
			},
		],
	}),
)
Clients can discover available prompts, retrieve their templates, and supply arguments to customize the resulting messages. Prompts can include not just text, but also images, audio, or references to server-managed resources—enabling multi-modal and context-rich interactions.
This exercise will introduce you to MCP's prompt capabilities, showing how to declare prompt support, register prompts with arguments, and return structured messages for downstream use.
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