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Product Hunt MCP Server

Provides Product Hunt data (posts, comments, users, topics, collections) via MCP for integration with LLMs and agents.

Installation
Add the following to your MCP client configuration file.

Configuration

View docs
{
  "mcpServers": {
    "jaipandya-producthunt-mcp-server": {
      "command": "product-hunt-mcp",
      "args": [],
      "env": {
        "PRODUCT_HUNT_TOKEN": "your_token_here"
      }
    }
  }
}

You have a plug-and-play MCP server that connects Product Hunt data to any Model Context Protocol (MCP) client. It lets you fetch posts, collections, topics, users, votes, and comments, enabling AI assistants, chatbots, and automations to query Product Hunt in a structured way.

How to use

Install and run the MCP server, then configure your MCP client to reach it. You can run the server directly or inside Docker. The server authenticates using your Product Hunt API token, which you provide as an environment variable. Once running, you can query posts, comments, topics, users, and more from your MCP-enabled client.

To connect from your MCP client, specify the server as an MCP endpoint with the appropriate command to start the server and ensure the token is available in the environment. For example, you can run the server locally with the token set and then point your client to that local MCP instance.

How to install

Prerequisites: Python 3.10+ and a Product Hunt API token.

pip install uv

Install the MCP server from PyPI or via the offering package manager.

# Preferred: uv (fast, modern Python installer)
pip install uv

# Install the MCP server from PyPI
uv pip install product-hunt-mcp
# or
pip install product-hunt-mcp

Alternative installation from source or GitHub is supported via uvx when you want the latest main branch.

uv pip install 'git+https://github.com/jaipandya/producthunt-mcp-server.git'
# or
pip install 'git+https://github.com/jaipandya/producthunt-mcp-server.git'

Install locally from source if you prefer a local build.

uv pip install .
# or
pip install .

Set the token in your environment and start the server.

export PRODUCT_HUNT_TOKEN=your_token_here
product-hunt-mcp

Docker usage

You can run the MCP server inside Docker and provide the token through the container environment.

# Build the Docker image
docker build -t product-hunt-mcp .

# Run the Docker container (interactive for MCP)
docker run -i --rm -e PRODUCT_HUNT_TOKEN=your_token_here product-hunt-mcp

Connecting from MCP clients

Configure your MCP client to start the server and supply the token as an environment variable. Here is a representative configuration for a client that expects an MCP endpoint named product-hunt.

{
  "mcpServers": {
    "product-hunt": {
      "command": "product-hunt-mcp",
      "env": {
        "PRODUCT_HUNT_TOKEN": "your_token_here"
      }
    }
  }
}

Security and best practices

Your API token is sensitive. Do not share it or commit it to version control. When using Docker, avoid embedding the token directly in code; pass it through the environment as shown above.

Available tools

get_post_details

Get detailed information for a specific post by id or slug, including related data such as comments if requested.

get_posts

Retrieve a list of posts with filters like topic, order, count, and whether to include featured items.

get_comment

Fetch information about a single comment by its id.

get_post_comments

Obtain comments for a specific post, with options for ordering and limiting results.

get_collection

Get information about a specific collection by id or slug.

get_collections

List collections with filters such as featured, user_id, post_id, order, and count.

get_topic

Retrieve information about a topic by id or slug.

search_topics

Search topics by query with optional filters like followed_by_user_id, order, and count.

get_user

Get user information by id or username, with options for posts type and posts count.

get_viewer

Fetch information about the authenticated user.

check_server_status

Check server/API status and authentication state.