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Prompts

Prompts are user-driven templates that provide shortcuts and working examples for AI interactions. They help users get started quickly without figuring out the right way to phrase requests.

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How Prompts Work

Prompts can be consumed in two ways, depending on how the client is built:
ModeWho TriggersHow It Works
User-InvokedUser explicitly selectsSlash commands (/analyze), dropdown menus, keyboard shortcuts
Agent-SelectedAI agent picks automaticallyAgent browses available prompts and selects the best one for the task
Client Implementation Matters: Whether prompts are user-invoked or agent-selected depends entirely on how the client application is developed. Some clients expose prompts as slash commands, others let the AI agent discover and use them automatically.

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When to Use Prompts

  • Provide examples of how to use your MCP server effectively
  • Create shortcuts for common workflows users perform repeatedly
  • Include dynamic context that would be tedious to type manually
  • Onboard new users with working examples they can invoke immediately

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Basic Prompt

import { Prompt } from "@leanmcp/core";

export class PromptService {
  @Prompt({ description: "Customer support assistant" })
  supportAssistant() {
    return {
      messages: [{
        role: "user",
        content: {
          type: "text",
          text: "You are a helpful customer support agent. Be polite and helpful."
        }
      }]
    };
  }
}

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Prompt with Arguments

import { Prompt, SchemaConstraint } from "@leanmcp/core";

class CodeReviewInput {
  @SchemaConstraint({ description: "Code to review" })
  code!: string;

  @SchemaConstraint({ description: "Programming language" })
  language!: string;
}

export class CodeService {
  @Prompt({ description: "Generate code review prompt" })
  codeReview(input: CodeReviewInput) {
    return {
      messages: [{
        role: "user",
        content: {
          type: "text",
          text: `Review this ${input.language} code for bugs, style, and best practices:\n\n${input.code}`
        }
      }]
    };
  }
}

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Multi-message Prompt

Set up a conversation with context: 
@Prompt({ description: "Code review conversation" })
codeReviewer() {
  return {
    messages: [
      {
        role: "user",
        content: { type: "text", text: "You are a senior code reviewer." }
      },
      {
        role: "assistant", 
        content: { type: "text", text: "I'll review the code for bugs, style, and best practices." }
      }
    ]
  };
}

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Specialized Prompts

@Prompt({ description: "SQL query generator" })
sqlHelper(input: { schema: string }) {
  return {
    messages: [{
      role: "user",
      content: {
        type: "text",
        text: `You are a SQL expert. Generate queries for this schema:\n\n${input.schema}`
      }
    }]
  };
}

@Prompt({ description: "API documentation writer" })
apiDocWriter() {
  return {
    messages: [{
      role: "user",
      content: {
        type: "text",
        text: "You write clear, concise API documentation with examples."
      }
    }]
  };
}

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The Three MCP Primitives

Understanding when to use each:
PrimitiveControlPurposeExample
ToolsModel-drivenActions the AI performsSend email, create task, query API
ResourcesApplication-drivenContext data for AIFiles, preferences, schedules
PromptsUser-drivenTemplates users invoke/analyze, /review, /support

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Next Steps

Tools

Create actions AI can execute

Resources

Expose data to AI agents