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datasets_producthunt_makers_search

Search the Product Hunt makers dataset. Searches Product Hunt makers from the dataset id enum value producthunt-makers — public-profile records of the people who made products, with their footprint (products made, total upvotes, topics)…

Instructions

Search the Product Hunt makers dataset. Searches Product Hunt makers from the dataset id enum value producthunt-makers — public-profile records of the people who made products, with their footprint (products made, total upvotes, topics) for maker leaderboards. Public fields only. Sort enum: total_votes_desc, product_count_desc, followers_desc, relevance. Billing: this search costs 1 credit per record returned, with a minimum of 2 (an empty result costs 2, a full page of 100 costs 100).

Input Schema

NameRequiredDescriptionDefault
qNoFull-text query over maker name and headline, max 256 characters
pageNoPage number, defaults to 1
sortNoSort enum: total_votes_desc, product_count_desc, followers_desc, relevance
topicNoExact topic-slug the maker builds in, e.g. artificial-intelligence, max 128 characters
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
min_productsNoMinimum number of products made, 0 or greater
min_total_votesNoMinimum total upvotes across the maker's products, 0 or greater

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "total_votes_desc",
+  "product_count_desc",
+  "followers_desc",
+  "relevance"
+]
  2. Addedv1.5.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose important traits: public fields only and a precise billing model (1 credit per record, minimum 2, empty result costs 2, full page of 100 costs 100). That cost disclosure is genuinely useful and non-obvious. It does not cover auth/permissions or return shape, keeping it below 5.

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?

Front-loaded with the resource and content, then billing. Mostly tight, though the sort enum listing duplicates the schema's enum and is somewhat redundant. The billing sentence earns its place.

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

Completeness4/5

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

For a no-output-schema search tool, the description covers what is being searched, the field scope, sorting, and the cost model, which is enough for an agent to call it. It omits return-field detail and any routing to sibling datasets, but is otherwise complete.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all seven parameters including enums and constraints. The description restates the sort enum values but adds no syntax or meaning beyond what the schema provides, so the baseline 3 applies.

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

Purpose4/5

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

States a specific verb ('Search') and resource ('Product Hunt makers dataset'), and describes the record content (public-profile people who made products, with products made, total upvotes, topics). It does not explicitly name sibling tools like datasets_producthunt_makers_item or datasets_producthunt_products_search to route the agent, so it stops short of a 5.

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

Usage Guidelines3/5

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

The phrase 'for maker leaderboards' implies the intended use case, and the dataset-id mention implies it is the search entry point for makers. However, there is no explicit when-to-use vs when-not guidance and no mention of the related facets/item siblings, leaving routing to inference.

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

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