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Product Hunt Launches Feed MCP Server

Product Hunt Launches Feed MCP Server

Product Hunt Launches Feed MCP Server

An MCP server for the Mamba Labs Product Hunt Launches Feed actor on Apify. Pick the days, and get one row per Product Hunt launch with the maker names, the product's website and company domain, its X and LinkedIn links, votes, rank, and topics.

What's Inside

Related MCP server: mcp-producthunt

What it does

The actor reads Product Hunt's public daily launch pages and each launch's product page through the Apify proxy, with robots.txt obeyed. The public daily page shows the day's top featured launches. With your own Product Hunt developer token (product_hunt_token), the daily lists come from the official Product Hunt API instead, and include_unfeatured adds the launches Product Hunt did not feature. Filter by topic, keyword, or minimum votes, and use only_new on a schedule. Each day also returns one free summary row.

All of the work runs on Apify. This package is a thin client that starts the actor run, waits for it, and hands back the dataset rows.

Quick start

You need Node.js 18 or newer and an Apify account with an API token.

Add this to your Claude Desktop config (claude_desktop_config.json), Cursor, or any MCP client:

{
  "mcpServers": {
    "mamba-product-hunt-launches-feed": {
      "command": "npx",
      "args": ["-y", "@mambalabsdev/mcp-product-hunt-launches-feed"],
      "env": {
        "APIFY_TOKEN": "your-apify-token"
      }
    }
  }
}

Restart the client. The get_product_hunt_launches tool appears.

Prerequisites

Example prompts

  • "List yesterday's Product Hunt launches in AI with their company domains and makers."

  • "Which developer tools launched on Product Hunt this week with more than 200 votes?"

  • "Get the launches of 2026-10-27 into a table."

Tool

get_product_hunt_launches. One tool. Example arguments:

{
  "days_back": 1,
  "topics": [
    "Artificial Intelligence"
  ],
  "max_launches": 50
}

Inputs

Every buyer facing input of the actor. Omitted inputs take the actor's own defaults.

Input

Type

Required

Description

days_back

integer

no

The last N full days, Pacific time. Default 1.

dates

array

no

Specific days as YYYY-MM-DD, up to 31.

include_today

boolean

no

Also read today's list. Default false.

topics

array

no

Product Hunt topics by name or slug.

keywords

array

no

Words to find in the name, tagline, or topics.

min_votes

integer

no

Minimum votes. Default 0.

max_launches

integer

no

Launch rows per run. Default 100.

open_product_pages

boolean

no

Read product pages for website, domain, makers, socials. Default true.

only_new

boolean

no

Skip launches an earlier run returned. Default false.

state_key

string

no

Name for the only new launches memory.

product_hunt_token

string

no

Your own Product Hunt developer token, optional.

include_unfeatured

boolean

no

With your token, add unfeatured launches. Default false.

max_charge_usd

string

no

Optional cost cap for the run.

Pricing

Pay per event on Apify, billed to your Apify account. Prices in USD.

Event

Free plan

Bronze

Silver

Gold

launch-row, per launch delivered

$0.00425

$0.004038

$0.003825

$0.003613

apify-actor-start, per run start

$0.002

$0.002

$0.002

$0.002

Day summary rows, input refusal rows, launches whose product page could not be read, and launches skipped with only_new are free. Mamba Labs pays the platform costs of the run, Apify proxy traffic included.

Full actor documentation

apify.com/mambalabs/product-hunt-launches-feed

License

MIT. See LICENSE.

Available Tools

1 tool
get_product_hunt_launchesGet Product Hunt launchesA
Read-onlyIdempotent

Get the Product Hunt launches of the days you pick, one flat row per launch: name, tagline, daily rank, votes, comments, topics, maker names with their headlines, maker profile links, the product's website and company domain, and its X and LinkedIn links, plus one free summary row per day. Filter by topic, keyword, or minimum votes; only_new returns only launches an earlier run did not. Public pages are read through the Apify proxy with robots.txt obeyed; an optional Product Hunt developer token of your own reads the daily lists from the official API and adds unfeatured launches. A run that does not succeed is returned as an error with its run ID. Read-only; requires an APIFY_TOKEN and is billed per launch row.

NameRequiredDescriptionDefault
datesNoSpecific launch days as YYYY-MM-DD, up to 31.
topicsNoProduct Hunt topics by name or slug, for example Artificial Intelligence, developer-tools.
keywordsNoWords to find in the launch name, tagline, or topics.
only_newNoSkip launches an earlier run with the same state_key returned (free). Default false.
days_backNoThe last N full days, in Product Hunt's day (Pacific time). 1 is yesterday. Default 1. Ignored when dates is set.
min_votesNoKeep launches with at least this many votes. Default 0.
state_keyNoOptional name for the only new launches memory.
max_launchesNoLaunch rows per run. Default 100.
include_todayNoAlso read today's list, which is still filling. Default false.
max_charge_usdNoOptional. The run stops before a charge that would pass this amount.
include_unfeaturedNoNeeds product_hunt_token. Also return unfeatured launches. Default false.
open_product_pagesNoRead each product page for website, domain, makers, and socials. Default true.
product_hunt_tokenNoOptional. Your own Product Hunt developer token, for the official API route. Never stored or logged.

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses the access path (Apify proxy, robots.txt obeyed), auth requirement (APIFY_TOKEN), per-launch-row billing, the max_charge_usd stop guard, that the token is never stored or logged, and that failed runs surface as errors with a run ID. That is substantial context an agent cannot get from the annotations alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a dense paragraph but front-loaded with the core purpose and output shape before filters and operational caveats. Nearly every clause carries information, though the run-on sentence structure and stacking of billing/auth notes makes it slightly harder to scan than it needs to be.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 13 all-optional parameters and no output schema, the description compensates by describing the returned row fields, the free summary row, the two access routes, and failure behavior. An agent has enough to decide whether to call it and to predict the response without a schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: only_new's memory semantics (free), include_unfeatured's dependency on product_hunt_token, and the effect of open_product_pages on which fields appear. It goes beyond restating the schema without fully explaining every parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource ('Get the Product Hunt launches of the days you pick') and then enumerates the exact row shape returned, including daily rank, votes, makers, and socials. There are no siblings to disambiguate, and the flat-row-per-launch output model plus free summary row is stated explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It names the selection mechanisms (dates, days_back, topics, keywords, min_votes) and explains the conditional modes: only_new returns launches an earlier run did not, and the optional developer token adds unfeatured launches. What's missing is any explicit statement of when not to use it or trade-offs between the date-selection paths, so it stops short of full 5-level guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedget_product_hunt_launches

TDQS

A4.6/5.0

Scored across 1 tool

Disambiguation5/5

There is only one tool, so there is no possibility of misselection between overlapping tools. Its purpose (fetch Product Hunt launch rows for chosen days with filters) is stated unambiguously in the description.

Naming Consistency5/5

The single name get_product_hunt_launches follows a clear verb_noun snake_case convention. With one tool there is no risk of mixed conventions, and the name is self-describing.

Tool Count4/5

The domain is a single read-only feed operation, and the one tool absorbs filtering, topic/keyword/vote thresholds, and incremental 'only_new' behavior, so it genuinely earns its place rather than being a trivial wrapper. It is still a one-tool surface, which feels thin if consumers later want per-product detail or comment retrieval.

Completeness4/5

For a feed reader the surface is largely complete: daily rows, maker metadata, links, topic/keyword/vote filters, incremental runs, and error reporting with run IDs. Minor gaps remain around deeper per-launch detail (e.g., comment bodies or multi-day historical backfill beyond the picked days), but core read workflows are covered.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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