producthunt-mcp-search

# producthunt-mcp-research `producthunt-mcp-research` is a powerful MCP-based search platform for Product Hunt data with AI-powered natural language queries. Built with TypeScript and following modern software architecture principles, it provides a robust foundation for semantic search and data exploration through AI assistants. ## What is producthunt-mcp-research? `producthunt-mcp-research` is a monorepo-based application that consists of several specialized modules: ```mermaid graph TB subgraph "AI Integration" M[MCP Server<br/>mcp-server-qdrant] AI[AI Assistant] end subgraph "Storage Layer" L[Qdrant<br/>Local Vector DB] end subgraph "Core Modules" F[Fetcher<br/>Product Hunt API] R[Repository<br/>Embedding + Qdrant] O[Orchestrator<br/>CLI + Coordination] S[Shared<br/>Types + Utils] end subgraph "Data Sources" PH[Product Hunt API<br/>GraphQL] end %% Core Modules <--> Storage Layer/AI Integration F -.->|depends on| S R -.->|depends on| S O -.->|depends on| S O -.->|uses| F O -.->|uses| R R -.->|connects to| L M -.->|queries| L F -.->|fetches from| PH M -.->|serves| AI S ~~~ PH ``` - **Fetcher**: Handles data retrieval from Product Hunt API using GraphQL - **Repository**: Manages data persistence with Qdrant and generates embeddings using Transformers.js - **Orchestrator**: Provides CLI interface and coordinates the data ingestion workflow - **Shared**: Contains common utilities, types, and logging used across modules ## Key Benefits ### Local-First Design Designed for local personal use only, **enabling you to set up a powerful analysis and search platform for free on your environment** with Qdrant's efficient vector search capabilities. ### AI-Powered Search Integrates with MCP (Model Context Protocol) to enable natural language queries through AI assistants, making data exploration accessible to everyone. ## Use Cases `producthunt-mcp-research` is perfect for: - **Business Opportunity Discovery**: Using AI-powered search to identify emerging trends and market needs - **Product Research**: Gathering insights about product launches and user engagement through natural language queries - **Competitive Intelligence**: Monitoring competitor products and their performance with semantic search - **Market Analysis**: Understanding user pain points and willingness-to-pay signals through comment analysis ## Important Note This tool is designed for **local personal use only**. Product Hunt's API terms of service prohibit commercial use, and this repository is provided as open source software for individual analysis and research purposes. ## Setup ### Prerequisites - Node.js (v18 or higher) - pnpm (v8 or higher) - Product Hunt API token ### Installation 1. **Clone the repository** ```bash git clone https://github.com/aoki-h-jp/producthunt-mcp-research.git cd producthunt-mcp-research ``` 2. **Install dependencies** ```bash pnpm install ``` 3. **Configure environment variables** Copy the example environment file: ```bash cp .env.example .env ``` Edit `.env` and **replace** `your_developer_token_here` with your actual Product Hunt API token: ```bash PH_API_TOKEN=your_actual_token_here ``` 📖 **Get your API token from**: https://api.producthunt.com/v2/oauth/applications ⚠️ **Important**: Make sure to use your actual API token, not the placeholder value! 4. **Build the project** ```bash pnpm run build ``` ⚠️ **Note**: The build will fail if the `.env` file is not properly configured. Make sure you have set your `PH_API_TOKEN` before running the build command.