@mcpflow.io/mcp-mentor-mcp-server/examples/README.md

一个模型上下文协议服务器，通过基于人工智能的Deepseek-Reasoning R1导师能力为LLM代理提供第二意见，包括代码审查、设计批判、写作反馈以及通过Deepseek API进行创意头脑风暴。

# Mentor MCP Server Tool Examples

This directory contains example requests and responses for each tool provided by the mentor-mcp-server. These examples demonstrate the capabilities and expected output format of each tool.

## Tools

### 1. [Second Opinion](second-opinion.md)
Provides a second opinion on a user's request by analyzing it with an LLM and listing critical considerations. The example shows analysis of building a secure authentication system.

### 2. [Code Review](code-review.md)
Provides a code review for a given file or code snippet, focusing on potential bugs, style issues, performance bottlenecks, and security vulnerabilities. The example shows a review of the code review tool's own implementation.

### 3. [Design Critique](design-critique.md)
Offers a critique of a design document, UI/UX mockup, or architectural diagram, focusing on usability, aesthetics, consistency, accessibility, and potential design flaws. The example shows analysis of a cloud monitoring dashboard design.

### 4. [Writing Feedback](writing-feedback.md)
Provides feedback on a piece of writing, such as an essay, article, or technical documentation, focusing on clarity, grammar, style, structure, and overall effectiveness. The example shows feedback on a rate limiting implementation guide.

### 5. [Brainstorm Enhancements](brainstorm-enhancements.md)
Generates creative ideas for improving a given concept, product, or feature, focusing on innovation, feasibility, and user value. The example shows enhancement ideas for an AI-powered code review tool.

## Production Readiness

All tools have been tested and demonstrate:
- Proper input validation and error handling
- Comprehensive and well-structured responses
- Consistent output formatting
- Practical applicability to real-world scenarios
- Deep domain expertise in their respective areas

The examples serve as both documentation and test cases, showing the expected behavior and quality of responses for each tool.