RT-Prompt-MCP
RT-Prompt-MCP is a server that provides domain-specific prompt suggestions to enhance LLM-generated content across various development and design tasks:
Backend Development: Suggestions based on context, programming language, and database type
Frontend Development: Prompts tailored to framework, device type, and context
UI Design: Design conversion prompts considering platform and design type
General Scenarios: Task-specific prompts for code or document generation
RT CRUD Standards: Development prompts following Rongtong backend standards
Feishu Integration: Specialized prompts for Feishu-related use cases
Provides specialized prompt suggestions for MySQL database design and development tasks
Offers tailored prompt suggestions for PostgreSQL database design and development
Delivers frontend-specific prompt suggestions optimized for React framework development
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@RT-Prompt-MCP get backend suggestions for creating a user authentication API with Node.js and MongoDB"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
RT-Prompt-MCP
RT-Prompt-MCP 是一个基于 Model Context Protocol (MCP) 的服务器,专注于提供开发和设计相关的提示词补充建议。
功能特点
提供特定领域的提示词补充,帮助 LLM 生成更符合要求的内容
支持后端开发、前端开发和通用场景的提示词
易于集成到支持 MCP 协议的客户端
使用 TypeScript 开发,类型安全
Related MCP server: Git Prompts MCP Server
安装
Installing via Smithery
To install rt-prompt-mcp-server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @yuyao1999/rt-prompt-mcp-server --client claudeManual Installation
全局安装:
npm install -g rt-prompt-mcp使用方法
作为命令行工具运行
安装后,直接运行:
rt-prompt-mcp作为 MCP Server 与 MCP 客户端集成
在支持 MCP 协议的应用中(如 Claude Desktop)配置:
{
"mcpServers": {
"rt-prompt-mcp": {
"command": "rt-prompt-mcp",
"args": []
}
}
}工具说明
该 MCP Server 提供五个主要工具:
get_backend_suggestions: 获取后端开发相关的提示词补充
context: 当前上下文或任务描述databaseType: 数据库类型(如 MySQL、PostgreSQL 等)language: 编程语言(如 Java、Python 等)
get_frontend_suggestions: 获取前端开发相关的提示词补充
context: 当前上下文或任务描述framework: 前端框架(如 React、Vue 等)deviceType: 设备类型(如移动端、桌面端等)
get_general_suggestions: 获取通用场景的提示词补充
context: 当前上下文或任务描述taskType: 任务类型(如代码生成、文档生成等)
get_ui_design_suggestions: 获取 UI 设计图转化相关的提示词补充
context: 当前上下文或任务描述designType: 设计类型(如线框图、高保真原型图等)platform: 平台类型(如 Web、iOS、Android 等)
get_rt_crud_suggestions: 获取荣通后端标准 CRUD 开发规范提示词。
base_path(可选): Java/Kotlin 根包路径,例如 'com.example.myapp' 或 'cn.teamy'。如果提供,将替换提示中默认的 'cn.teamy'。请使用点分隔路径。
get_feishu_prompt: 获取飞书相关的提示词
prompt_name: 提示词名称,如'UI 转化提示词'、'AI 生成 UI-3D 风格'等
示例
例如,要获取 MySQL 数据库设计的建议:
使用 get-backend-suggestions 工具,并提供以下参数:
- context: "创建用户和订单的数据库表结构"
- databaseType: "MySQL"
- language: "SQL"开发
前提条件
Node.js 16+
npm 或 yarn
本地开发
克隆仓库:
git clone https://github.com/yourusername/rt-prompt-mcp.git cd rt-prompt-mcp安装依赖:
npm install构建项目:
npm run build本地测试:
npm start
许可证
MIT
Available Tools
6 toolsget_backend_suggestionsD
Inputs: context, databaseType, language.
get_feishu_promptD
Inputs: prompt_name (required).
get_frontend_suggestionsD
Inputs: context, deviceType, framework.
get_general_suggestionsD
Inputs: context, taskType.
get_rt_crud_suggestionsD
Inputs: base_path.
get_ui_design_suggestionsD
Inputs: context, designType, platform.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 6 tools
The tools are clearly distinguished by their target domains (backend, frontend, general, RT-CRUD, UI design, Feishu), making misselection unlikely. However, the lack of descriptions means the exact boundaries between 'general' and domain-specific suggestions are unclear, which could cause minor confusion.
All tools follow a perfect 'get_[domain]_suggestions' pattern, with consistent snake_case and verb-noun structure. This predictability makes it easy for agents to understand and use the toolset without naming-related errors.
Six tools is a reasonable number for a prompt suggestion server, covering multiple domains without being overwhelming. It could be slightly thin if more granular domains are needed, but the scope appears well-defined for common development areas.
The toolset is severely incomplete as it only provides 'get' operations with no ability to create, update, delete, or manage prompts. For a prompt management server, this represents significant gaps that will limit agent workflows and cause dead ends in multi-step tasks.
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