mcp-prompts-guide

What this skill does

You are an expert in creating MCP prompts using the rmcp crate, with knowledge of prompt design, template systems, and context management.

## Your Expertise

You guide developers on:
- Prompt design and templates
- Dynamic argument handling
- Context injection patterns
- Multi-turn conversations
- Prompt chaining
- Testing prompts

## What are MCP Prompts?

**Prompts** are predefined message templates that MCP servers provide to guide AI interactions. They help users start conversations with the right context and structure.

### Prompt Characteristics

- **Templated**: Use placeholders for dynamic values
- **Contextual**: Include relevant information
- **Actionable**: Guide toward specific tasks
- **Reusable**: Work across multiple conversations
- **Discoverable**: Listed for user selection

## Implementing Prompts

### Basic Prompt Structure

```rust
use rmcp::prelude::*;
use serde::{Deserialize, Serialize};
use schemars::JsonSchema;

#[derive(Clone)]
struct PromptService;

impl PromptService {
    async fn list_prompts(&self) -> Vec<PromptInfo> {
        vec![
            PromptInfo {
                name: "analyze_code".to_string(),
                description: Some("Analyze code for issues and improvements".to_string()),
                arguments: vec![
                    PromptArgument {
                        name: "language".to_string(),
                        description: Some("Programming language".to_string()),
                        required: true,
                    },
                    PromptArgument {
                        name: "focus".to_string(),
                        description: Some("Focus area (performance, security, style)".to_string()),
                        required: false,
                    },
                ],
            },
        ]
    }

    async fn get_prompt(
        &self,
        name: &str,
        arguments: HashMap<String, String>,
    ) -> Result<PromptResponse, Error> {
        match name {
            "analyze_code" => {
                let language = arguments.get("language")
                    .ok_or_else(|| Error::MissingArgument("language".to_string()))?;
                let focus = arguments.get("focus").map(|s| s.as_str()).unwrap_or("general");

                let messages = vec![
                    PromptMessage {
                        role: Role::User,
                        content: format!(
                            "I need you to analyze {} code with a focus on {}. \
                             Please review the code carefully and provide:\n\
                             1. Issues found\n\
                             2. Recommendations\n\
                             3. Code examples for improvements",
                            language, focus
                        ),
                    },
                ];

                Ok(PromptResponse {
                    description: Some("Code analysis prompt".to_string()),
                    messages,
                })
            }
            _ => Err(Error::PromptNotFound(name.to_string())),
        }
    }
}
```

## Prompt Design Patterns

### Pattern 1: Code Review Prompts

```rust
impl PromptService {
    fn create_code_review_prompt(&self, args: &HashMap<String, String>) -> PromptResponse {
        let language = args.get("language").map(|s| s.as_str()).unwrap_or("any");
        let file_path = args.get("file_path");

        let mut context = format!(
            "You are an expert code reviewer for {} projects.\n\n",
            language
        );

        if let Some(path) = file_path {
            context.push_str(&format!("File: {}\n\n", path));
        }

        context.push_str(
            "Please review the code for:\n\
             - Bugs and logic errors\n\
             - Performance issues\n\
             - Security vulnerabilities\n\
             - Code style and best practices\n\
             - Test coverage gaps\n\n\
             Provide specific line numbers and actionable recommendations."
        );

        PromptResponse {
            description: Some("Comprehensive code review".to_string()),
            messages: vec![
                PromptMessage {
                    role: Role::User,
                    content: context,
                },
            ],
        }
    }
}
```

### Pattern 2: Task-Specific Prompts

```rust
impl PromptService {
    fn create_api_design_prompt(&self, args: &HashMap<String, String>) -> PromptResponse {
        let api_type = args.get("api_type").map(|s| s.as_str()).unwrap_or("REST");
        let domain = args.get("domain").map(|s| s.as_str()).unwrap_or("general");

        PromptResponse {
            description: Some(format!("Design {} API for {}", api_type, domain)),
            messages: vec![
                PromptMessage {
                    role: Role::System,
                    content: format!(
                        "You are an expert API architect specializing in {} APIs.",
                        api_type
                    ),
                },
                PromptMessage {
                    role: Role::User,
                    content: format!(
                        "I need to design a {} API for a {} application.\n\n\
                         Please help me:\n\
                         1. Define the resource models\n\
                         2. Design the endpoints\n\
                         3. Specify request/response formats\n\
                         4. Consider authentication and authorization\n\
                         5. Plan for versioning and backwards compatibility\n\n\
                         Focus on best practices and industry standards.",
                        api_type, domain
                    ),
                },
            ],
        }
    }
}
```

### Pattern 3: Interactive Learning Prompts

```rust
impl PromptService {
    fn create_learning_prompt(&self, args: &HashMap<String, String>) -> PromptResponse {
        let topic = args.get("topic").ok_or(Error::MissingArgument("topic".to_string())).unwrap();
        let level = args.get("level").map(|s| s.as_str()).unwrap_or("beginner");

        PromptResponse {
            description: Some(format!("Learn {} at {} level", topic, level)),
            messages: vec![
                PromptMessage {
                    role: Role::System,
                    content: format!(
                        "You are a patient and knowledgeable teacher of {}. \
                         Adapt your explanations to a {} level.",
                        topic, level
                    ),
                },
                PromptMessage {
                    role: Role::User,
                    content: format!(
                        "I want to learn about {}. I'm at a {} level.\n\n\
                         Please:\n\
                         1. Start with the fundamentals\n\
                         2. Provide clear examples\n\
                         3. Build complexity gradually\n\
                         4. Check my understanding as we go\n\
                         5. Suggest exercises\n\n\
                         Let's begin!",
                        topic, level
                    ),
                },
            ],
        }
    }
}
```

### Pattern 4: Context-Rich Prompts

```rust
impl PromptService {
    async fn create_project_prompt(
        &self,
        args: &HashMap<String, String>,
        project_context: &ProjectContext,
    ) -> PromptResponse {
        let task = args.get("task").unwrap();

        let context = format!(
            "# Project Context\n\n\
             Project: {}\n\
             Language: {}\n\
             Framework: {}\n\
             Dependencies: {}\n\n\
             # Task\n\n\
             {}\n\n\
             # Guidelines\n\n\
             - Follow the project's existing patterns\n\
             - Maintain code style consistency\n\
             - Update tests and documentation\n\
             - Consider existing