MCP Prompts and Message Flows
Document MCP prompt templates: typed arguments with completion support, expected message flows with role and content type, worked examples with argument-to-output mappings, and prompt engineering patterns for consistent AI behaviour.
Prompts are reusable conversation templates that standardise how AI assistants interact with specific domains. Unlike tools (which perform actions) and resources (which provide data), prompts define the structure and flow of AI conversations. They are user-controlled: the end user selects which prompt to activate, and the MCP client renders it with the provided arguments.
When to Use Prompts
Prompts are valuable whenever you want consistent, repeatable AI behaviour across different contexts.
Arguments
Prompt arguments are named parameters that the user provides when activating the prompt. They are substituted into the expected messages using {argumentName} placeholders.
Expected Messages
Expected messages define the conversation structure when the prompt is rendered. Each message specifies a role, content type, and either a template or description.
Template Text
The template text field contains the actual message content with argument placeholders. Placeholders use curly brace syntax: {argumentName}.
Based on the following customer preferences, recommend up to
{maxResults} products from the {productCategory} category.
Customer preferences: {customerPreferences}
For each recommendation, provide:
1. Product name and key features
2. Why this product matches their preferences
3. Price and availability
4. Any relevant alternativesWorked Examples
Prompt examples tie everything together: they show specific argument values mapped to the expected conversation output. Each example includes argument values and the expected messages the AI should produce.
Prompt Engineering Patterns
Effective MCP prompts follow several established patterns.
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