🚀 Product Hunt MCP Server

Created By
MCP-Mirrora year ago
Mirror of
Overview

What is Product Hunt MCP Server?

Product Hunt MCP Server is a plug-and-play server that connects Product Hunt's API to any LLM or agent that speaks the Model Context Protocol (MCP). It is designed for AI assistants, chatbots, and automations that require access to Product Hunt data.

How to use Product Hunt MCP Server?

To use the server, install it via pip, set your Product Hunt API token as an environment variable, and run the server using the command product-hunt-mcp. You can also integrate it with tools like Claude Desktop or Cursor.

Key features of Product Hunt MCP Server?

  • Access to detailed information on posts, comments, collections, topics, and users from Product Hunt.
  • Ability to search and filter data by various parameters such as topic and date.
  • Built with FastMCP for speed and compatibility.

Use cases of Product Hunt MCP Server?

  1. Building AI assistants that provide insights from Product Hunt.
  2. Creating dashboards that display trending products and user interactions.
  3. Automating data retrieval for analysis or reporting purposes.

FAQ from Product Hunt MCP Server?

  • What is the Model Context Protocol (MCP)?

MCP is a protocol that allows different agents to communicate and share data seamlessly.

  • Is there a rate limit for using the Product Hunt API?

Yes, the API has rate limits that the server respects, and it will inform you when limits are reached.

  • Can I run the server using Docker?

Yes, the server can be run using Docker with the appropriate configuration.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
MCP-Mirror
Star
0
Language
Python
License
-

Recommend Servers

View All
Voyei

2 hours ago
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago