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Furqan Ahmad
Furqan Ahmad

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🚀 MVP Agent: Your AI-Powered Blueprint Generator for Rapid Innovation

#mcp #gemini #agents #buildinpublic

Transforming Ideas into Production-Ready MVPs in Minutes

In the fast-paced world of startups and product development, the journey from a nascent idea to a concrete, actionable plan is often fraught with challenges. Market research is time-consuming, competitive analysis is complex, and synthesizing insights into a coherent, implementation-ready specification can take weeks. This is where the MVP Agent steps in.

Born from the Model Context Protocol (MCP) 1st Birthday Hackathon 2025, the MVP Agent is an autonomous AI system designed to revolutionize early-stage product development. It transforms any startup idea – articulated in a single paragraph – into eight comprehensive, production-ready markdown files, delivering over 18,000 words of detailed specifications in mere minutes.

✨ Key Features: What You Get

The MVP Agent delivers a complete suite of documentation, meticulously structured for both human teams and AI coding agents:

  • 📝 Overview.md: High-level MVP overview and usage guidance.
  • 📋 Features.md: Prioritized feature requirements with user personas.
  • 🏗️ Architecture.md: Technical stack, component tables, API surface, and scalability plans.
  • 🎨 Design.md: UI/UX guidelines, design system, and accessibility standards.
  • 🗺️ User Flow.md: Complete, step-by-step user journeys with decision trees.
  • 📅 Roadmap.md: Detailed launch plan, milestones, and technical debt strategy.
  • 💼 Business_model.md: Comprehensive business model, unit economics, and go-to-market strategy.
  • 🧪 Testing_plan.md: Testing strategy, test cases, and quality gates.
  • 📦 ZIP Download: All generated files packaged for easy sharing and distribution.

Quality Highlights:

  • Production-Grade: Specifications are detailed enough for direct implementation by senior developers or AI coding agents.
  • AI-Friendly: Structured formatting optimized for consumption by advanced LLMs.
  • Comprehensive: Covers user, business, and technical perspectives in depth.
  • No Dependencies: Pure markdown, free from rendering errors often associated with external diagramming tools.

🧠 How It Works: The Autonomous Agent in Action

The MVP Agent operates as a sophisticated multi-phase AI agent, orchestrated to mimic the workflow of an experienced product manager, market researcher, and solution architect. Powered by Google Gemini and custom Model Context Protocol (MCP) microservices, it operates through the following stages:

  1. Phase 1: Intent Understanding & Query Planning 📝

    • The agent begins by deeply analyzing your single-paragraph startup idea. It identifies the core problem, target users, potential value propositions, and success metrics.
    • Based on this understanding, it dynamically generates 7 highly effective and focused search queries designed to extract precise market and competitive intelligence.

  2. Phase 2: Real-time Market Research 🔍

    • Utilizing the Google Custom Search MCP microservice, the agent executes these queries to conduct real-time market research, analyzing competitors, identifying existing solutions, market trends, and gathering user feedback and pain points from diverse web sources.

  3. Phase 3: Analysis, Synthesis & Insight Generation 📊

    • The raw research data is then processed and synthesized. The agent identifies critical market gaps, uncovers opportunities, maps feature requirements directly to user needs, and determines the optimal technical and business architecture for the proposed MVP.

  4. Phase 4: Comprehensive Blueprint Generation ✨

    • This is where the magic culminates. The agent leverages carefully crafted prompts to generate the eight detailed, structured markdown documents that form the complete MVP blueprint.
    • During this phase, the Markdownify MCP ensures consistent and production-grade formatting, while the File Manager MCP handles the robust packaging of all generated content into a convenient downloadable ZIP file.

🚀 The Power of AI-Friendly Outputs and MCP

A core differentiator of the MVP Agent lies in its commitment to AI-friendly outputs and its innovative use of the Model Context Protocol (MCP).

The generated markdown specifications are not just human-readable; they are meticulously structured, using hierarchical headings, clear tables, and explicit sections for rationale and agent guidance. This design makes them highly consumable by other Large Language Models (LLMs) and AI coding assistants like Cursor, Windsurf, or Claude Code. An AI agent can parse these outputs with unprecedented accuracy, directly translating the specifications into code, infrastructure, or further detailed plans, thereby accelerating development cycles exponentially.

The Model Context Protocol (MCP) serves as the architectural backbone for this interoperability and robustness. By encapsulating specialized functionalities into independent microservices (the MCPs), the MVP Agent achieves:

  • Modularity: Each tool (Search, File Management, Markdown Formatting) is a self-contained unit, promoting clear separation of concerns.
  • Resilience: Failures in one MCP do not cascade through the entire system, and fallbacks can be implemented at the orchestration layer.
  • Scalability: Individual MCPs can be scaled independently based on demand.
  • Extensibility: New AI-powered tools and integrations can be seamlessly added as new MCPs, expanding the agent's capabilities without disrupting its core logic.

This strategic choice of architecture ensures that the MVP Agent is not just a tool, but a foundational component in a future where AI agents collaboratively build complex software systems.

🏗️ Architecture: A Robust Microservice Foundation

The MVP Agent is built on a resilient, modular architecture that emphasizes scalability, maintainability, and extensibility.

  • Core Application (Python/Gradio): The main application logic and user interface are written in Python, leveraging the Gradio framework for a clean, interactive, and mobile-responsive web experience. This orchestrates the entire agent workflow and communicates with the underlying microservices.
  • Google Gemini API: The backbone of the agent's intelligence, Gemini models handle the complex reasoning, analysis, and content generation tasks.
  • Custom MCP Microservices (FastAPI): The agent's specialized tools are encapsulated as independent FastAPI services, managed by src/mcp_process_manager.py. These ensure modularity and efficient resource utilization:
    • file-manager-mcp (Port 8081): Responsible for file creation, markdown validation, and robust ZIP packaging. It now dynamically creates ZIP archives directly from in-memory content provided by the main agent.
    • google-search-mcp (Port 8082): Executes structured Google Custom Search queries to gather real-time market data.
    • markdownify-mcp (Port 8083): Ensures all generated markdown content adheres to a consistent, clean, and AI-friendly format.
  • Shared State & Concurrency: The Gradio UI runs the agent's main logic in a separate thread, providing real-time status updates and ensuring the UI remains responsive. Shared state is carefully managed to maintain consistency.
  • Stateless by Design: Optimized for cloud deployments like Hugging Face Spaces, the architecture prioritizes in-memory markdown generation and temporary ZIP storage with automatic cleanup, minimizing persistent storage requirements and eliminating race conditions in multi-user environments.

🚀 How to Use the MVP Agent

Getting your MVP blueprint is straightforward:

  1. Access the App: Navigate to the live Hugging Face Space (link below) or run the application locally (see setup instructions).
  2. Enter Your Idea: In the provided text box, describe your startup idea in a single paragraph. Be clear and concise.
  3. Configure (Optional): Use the "Advanced Configuration" accordion to specify a target platform, preferred tech stack, or any key constraints (e.g., "Must be Open Source," "Max $50/month hosting").
  4. Generate Blueprint: Click the "🎯 Generate MVP Blueprint" button.
  5. Monitor Progress: Observe the real-time status updates and elapsed time in the "Agent Status" section.
  6. Review & Download: Once generation is complete, review the 8 markdown files across the interactive tabs. You can then download all files as a convenient ZIP archive.

🛠️ Local Setup and Installation

To run the MVP Agent locally, follow these steps:

  1. Clone the Repository:

    git clone [YOUR_GITHUB_REPO_URL]
cd mvp-agent

  2. Create and Activate a Virtual Environment:

    python -m venv venv
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

  3. Install Dependencies:

    pip install -r requirements.txt

  4. Configure API Keys: Create a .env file in the project root and add your API keys:

    GEMINI_API_KEY=your_gemini_api_key
GOOGLE_API_KEY=your_google_cloud_api_key        # Optional, for enhanced search
GOOGLE_SEARCH_ENGINE_ID=your_custom_search_engine_id # Optional, for enhanced search

*   Get your `GEMINI_API_KEY` from [Google AI Studio](https://makersuite.google.com/app/apikey).
*   `GOOGLE_API_KEY` and `GOOGLE_SEARCH_ENGINE_ID` are required for real-time web research; otherwise, the agent uses placeholder data.
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  1. Run the Application:

    python app.py

    The app will start all necessary MCP servers and launch the Gradio UI in your browser.

🌐 Deployment: Hugging Face Spaces

The MVP Agent is designed for seamless one-click deployment to Hugging Face Spaces. Simply fork the repository or create a new Space from it, and configure your API keys as "Space Secrets." The application handles the rest, including starting the MCP servers.

🤝 Contributing to the Vision

We welcome contributions to enhance the MVP Agent! Ideas for future improvements include:

  • Adding more MCP tools (e.g., pricing intelligence, analytics).
  • Supporting additional export formats (PDF/DOCX).
  • Implementing caching for research results.
  • Developing basic user authentication and saved project functionality.
  • Extending support to non-SaaS product types.

To contribute: Fork the repository, create a feature branch, make your changes, and submit a pull request.

🔗 Resources

Built with ❤️ using Google Gemini, Gradio, and Model Context Protocol.

Top comments (1)

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sriblanda profile image
Sri Blanda •

Love this! Fun fact: 42% of startups fail from “no market need” — nice to see an agent that can find that out before you've named the company and bought the domain.