Prompts are reusable instruction templates that AI agents can use to guide conversations or tasks. They provide structured ways to format requests and context. In mcp-nest, prompts are defined using the `@Prompt()` decorator.
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Prompts are reusable instruction templates that AI agents can use to guide conversations or tasks. They provide structured ways to format requests and context. In mcp-nest, prompts are defined using the
@Prompt()import { Injectable, Scope } from '@nestjs/common'; import { Prompt } from '@rekog/mcp-nest'; import { z } from 'zod'; @Injectable({ scope: Scope.REQUEST }) export class GreetingPrompt { @Prompt({ name: 'multilingual-greeting-guide', description: 'Simple instruction for greeting users in their native languages', parameters: z.object({ name: z.string().describe('The name of the person to greet'), language: z.string().describe('The language to use for the greeting'), }), }) getGreetingInstructions({ name, language }) { return { description: 'Greet users in their native languages!', messages: [ { role: 'user', content: { type: 'text', text: `Greet ${name} in their preferred language: ${language}`, }, }, ], }; } }
A prompt returns an object with:
Messages can have different roles:
@Prompt({ name: 'code-review-guide', description: 'Instructions for reviewing code', parameters: z.object({ codeLanguage: z.string(), focusArea: z.string(), }), }) getCodeReviewPrompt({ codeLanguage, focusArea }) { return { description: 'Guide for conducting thorough code reviews', messages: [ { role: 'system', content: { type: 'text', text: `You are an expert ${codeLanguage} code reviewer.`, }, }, { role: 'user', content: { type: 'text', text: `Please review this code focusing on: ${focusArea}`, }, }, ], }; }
Create complex conversation flows:
@Prompt({ name: 'interview-guide', description: 'Structured interview questions', parameters: z.object({ role: z.string().describe('Job role being interviewed for'), experience: z.string().describe('Years of experience'), }), }) getInterviewGuide({ role, experience }) { return { description: `Interview guide for ${role} position`, messages: [ { role: 'system', content: { type: 'text', text: 'You are conducting a technical interview. Be thorough but encouraging.', }, }, { role: 'assistant', content: { type: 'text', text: `Hello! I understand you're applying for a ${role} position with ${experience} years of experience.`, }, }, { role: 'user', content: { type: 'text', text: 'Yes, that's correct. I'm excited to discuss the role.', }, }, { role: 'assistant', content: { type: 'text', text: 'Great! Let's start with some technical questions relevant to your experience level.', }, }, ], }; }
Prompts support different content types:
{ role: 'user', content: { type: 'text', text: 'Your message here', }, }
{ role: 'user', content: { type: 'image', data: 'base64-encoded-image-data', mimeType: 'image/png', }, }
Build prompts based on business logic:
@Prompt({ name: 'task-planner', description: 'Creates task planning prompts based on complexity', parameters: z.object({ task: z.string(), complexity: z.enum(['simple', 'medium', 'complex']), }), }) getTaskPlannerPrompt({ task, complexity }) { const baseMessage = `Plan the following task: ${task}`; const complexityInstructions = { simple: 'Keep it straightforward with 2-3 steps.', medium: 'Break it down into clear phases with dependencies.', complex: 'Create a detailed plan with milestones, risks, and alternatives.', }; return { description: `Task planning for ${complexity} task`, messages: [ { role: 'system', content: { type: 'text', text: 'You are a project planning expert.', }, }, { role: 'user', content: { type: 'text', text: `${baseMessage}\n\n${complexityInstructions[complexity]}`, }, }, ], }; }
Run the playground server:
npx ts-node-dev --respawn playground/servers/server-stateful.ts
npx @modelcontextprotocol/[email protected] --cli http://localhost:3030/mcp --transport http --method prompts/list
Expected output:
{ "prompts": [ { "name": "multilingual-greeting-guide", "description": "Simple instruction for greeting users in their native languages" } ] }
npx @modelcontextprotocol/[email protected] --cli http://localhost:3030/mcp --transport http --method prompts/get --prompt-name multilingual-greeting-guide --prompt-args name=Alice --prompt-args language=es
Expected output:
{ "description": "Greet users in their native languages!", "messages": [ { "role": "user", "content": { "type": "text", "text": "Greet Alice in their preferred language: es" } } ] }
For interactive testing, use the MCP Inspector UI:
npx @modelcontextprotocol/[email protected]
Connect to
http://localhost:3030/mcpSee the complete example at:
playground/resources/greeting.prompt.ts