What is an MCP prompt?

MCP prompts are predefined message templates provided by Model Context Protocol servers to guide AI interactions with specific context, structure, and instructions.

Does this support multi-turn conversations?

Absolutely. It provides patterns for creating multi-turn debug sessions and interactive learning prompts where the AI initiates with a specific role and sequence.

Can I use dynamic arguments in my prompts?

Yes, the guide covers how to define and handle dynamic arguments (like language or focus area) to make prompts flexible and reusable across different user scenarios.

How does context injection work?

It demonstrates how to load external data, such as documentation, code examples, or project metadata, and inject it dynamically into the prompt messages for higher accuracy.

Which Rust crate does this guide focus on?

This skill specifically focuses on implementing prompts using the rmcp crate, providing types and traits for building MCP-compliant servers.

MCP Prompt Engineering Guide for Rust

Name: MCP Prompt Engineering Guide for Rust
Author: EmilLindfors

byEmilLindfors

Learning & Documentation

Master the creation of powerful Model Context Protocol (MCP) prompts using Rust to guide AI interactions with templates, dynamic arguments, and context injection.

About

This skill provides specialized expertise in designing and implementing MCP prompts using the rmcp Rust crate. It equips developers with patterns for templated messages, dynamic argument handling, and complex context management to ensure AI models receive the most relevant and structured information. Whether you are building code review tools, interactive learning systems, or context-aware task assistants, this guide offers ready-to-use implementation patterns and best practices for multi-turn conversations, few-shot prompting, and sophisticated context injection.

Key Features

Rust rmcp crate implementation examples
Multi-turn conversation and prompt chaining structures
0 GitHub stars
Advanced context injection patterns for documentation and project data
Template-based prompt design for reusable AI interactions
Dynamic argument handling for flexible user input

Use Cases

Building structured code review prompts for automated repository analysis
Designing interactive AI learning paths with progressive complexity levels
Implementing context-aware project assistants that reference local documentation