Product Hunt MCP

Product Introduction

1. Product Hunt MCP is a server implementation that bridges Product Hunt's API with AI systems through the Model Context Protocol (MCP). It enables programmatic access to Product Hunt data including posts, collections, topics, users, votes, and comments via standardized MCP communication.
2. The core value lies in its ability to transform Product Hunt's API into structured, LLM-compatible data streams while maintaining compatibility with popular AI tools like Claude Desktop and Cursor. It serves as middleware that abstracts API complexity while preserving full data fidelity for AI applications.

Main Features

1. The server provides comprehensive API coverage with tools to retrieve post details (including comments and votes), user profiles, collection metadata, and topic-specific content through standardized MCP endpoints. Each endpoint supports filtering by date ranges, popularity metrics, and topic associations.
2. Built-in schema validation ensures data consistency between Product Hunt's API responses and MCP requirements, automatically transforming API payloads into LLM-optimized formats while maintaining backward compatibility with raw API structures.
3. Native Docker support and preconfigured client integration scripts enable deployment in both local development environments and production infrastructure, with automatic rate limit handling that respects Product Hunt's API constraints (200 requests/hour default).

Problems Solved

1. Eliminates manual API integration work for AI developers by providing ready-to-use MCP endpoints that handle authentication, pagination, and data normalization for Product Hunt's REST API. This reduces typical integration time from days to minutes.
2. Specifically designed for AI/ML engineers building Product Hunt-powered features in LLM applications, product managers analyzing market trends through automated data collection, and developers creating bots that interact with community content.
3. Enables real-time analysis of product launches through automated post monitoring, sentiment analysis of comments at scale, and generation of trend reports using aggregated voting data - all accessible through natural language queries in MCP-compatible clients.

Unique Advantages

1. Unlike generic API wrappers, this implementation natively supports MCP's bidirectional streaming protocol, enabling real-time updates from Product Hunt to connected AI systems without polling overhead. This reduces latency for time-sensitive applications.
2. Implements adaptive pagination that automatically optimizes request batches based on LLM context window sizes, preventing data truncation while minimizing API calls. This balances completeness with rate limit compliance.
3. Combines FastMCP's performance optimizations (achieving <5ms overhead per request) with Product Hunt's official API client library, ensuring both speed and data accuracy. The hybrid architecture supports load balancing across multiple API tokens out of the box.

Frequently Asked Questions (FAQ)

1. How do I handle authentication with the Product Hunt API? The server requires a PRODUCT_HUNT_TOKEN environment variable containing a valid Developer Token from Product Hunt's API dashboard. This token must have read permissions for all accessed resources.
2. Can I run this server in Docker for production use? Yes, the included Dockerfile supports both development and production modes, with the production image being Alpine-based (87MB compressed) and including automatic health checks and connection pooling.
3. What happens when hitting Product Hunt's rate limits? The server implements exponential backoff with jitter, automatically queuing requests until the rate limit resets (typically hourly). Current rate limit status is exposed through the check_server_status tool.
4. How do I integrate with Claude Desktop? Add the provided configuration snippet to Claude's settings.json, ensuring the Python interpreter path matches your environment. The server uses port 7437 by default but will auto-select available ports if conflicts occur.
5. Does this support Product Hunt's upcoming API changes? The schema validation layer abstracts API version differences, with semantic versioning indicating breaking changes. Major API revisions will trigger minor version updates in the package.