vibe-plan-mcp

vibe-plan-mcp

3.2

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A Model Context Protocol (MCP) server designed to assist VibeCoder developers in selecting the ideal tech stack for their projects through a series of progressive questions and personalized recommendations.

Vibe Stack MCP šŸš€

A Model Context Protocol (MCP) server that helps VibeCoder developers choose the perfect tech stack for their projects through progressive questioning and personalized recommendations.

Perfect for rapid prototyping and getting tech stack recommendations based on your project requirements, budget, timeline, and technical comfort level.

What it does

This MCP server helps developers quickly choose optimal technology stacks without getting bogged down in technical analysis paralysis. Instead of researching dozens of frameworks, it asks smart questions about your project and gives you the modern, battle-tested stack that fits your needs.

Key Features

  • Jargon-free questioning: Uses simple language anyone can understand
  • Progressive elicitation: Gathers requirements step-by-step using MCP's elicitation spec
  • Platform-as-a-Service focus: Recommends easy-to-use platforms like Vercel, Supabase, Netlify
  • Practical guidance: Provides deployment guides and cost estimates
  • Beginner-friendly: Focuses on tools that minimize technical complexity

Installation

  1. Install dependencies:
uv pip install fastmcp
  1. Run the server:
python run_server.py
  1. Add to your MCP client configuration (e.g., Claude Desktop):
{
  "mcpServers": {
    "vibe-stack-planner": {
      "command": "python",
      "args": ["/path/to/vibe_coder_stack_planner/run_server.py"]
    }
  }
}

Tools Available

start_project_planning()

Initiates the interactive planning process through a series of simple questions:

  • What are you trying to build?
  • Who will use it and how?
  • What features do you need?
  • How many users do you expect?
  • What's your budget and technical comfort level?

recommend_stack(session_requirements?)

Provides tech stack recommendations based on gathered requirements. Can be used with or without the interactive process.

explain_recommendation(detail_level?)

Explains why specific technologies were recommended, with "basic" or "detailed" explanations.

get_deployment_guide(platform?)

Provides step-by-step deployment instructions tailored to your specific needs.

Example Usage

User: I want to build something but I don't know where to start technically.

AI: Let me help you plan the right tech stack! I'll use the vibe coder stack planner to ask you some simple questions.

[Uses start_project_planning tool]

Server: Let's start planning your project! First, tell me about your vision.
What problem are you trying to solve, or what idea do you want to build?

[Progressive questioning continues...]

Server: šŸŽ‰ Perfect! Based on what you've told me, here's my recommendation:

**Recommended Tech Stack:**
• Frontend: Next.js (React framework) for a modern web app
• Backend: Supabase (handles database, auth, and API automatically)  
• Hosting: Vercel (free tier covers most small projects)
• Domain: Namecheap or Google Domains (~$12/year)

**Why this works for you:**
I chose beginner-friendly tools that handle most technical details automatically. These tools let you build and deploy quickly. This stack has generous free tiers to keep costs minimal.

Architecture

The server uses:

  • FastMCP: High-level Python framework for MCP servers
  • Elicitation Spec: Latest MCP elicitation specification for interactive questioning
  • Rule-based recommendations: Analyzes requirements to suggest appropriate technologies
  • Progressive disclosure: Builds complexity gradually based on user comfort level

Supported Platforms

The server focuses on Platform-as-a-Service solutions:

  • Frontend: Vercel, Netlify, GitHub Pages
  • Backend: Vercel Functions, Netlify Functions, Supabase
  • Database: Supabase, PlanetScale, Firebase
  • Auth: Supabase Auth, Auth0, Clerk
  • Hosting: Vercel, Netlify, Render

Development

To extend or modify the server:

  1. Add new question types: Modify the elicitation flow in _ask_about_* functions
  2. Enhance recommendations: Update the _analyze_requirements function
  3. Add new platforms: Extend the recommendation logic and deployment guides
  4. Improve UI: The elicitation spec supports rich form controls

License

This project is open source. Feel free to fork, extend, and contribute!


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