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datasets_producthunt_makers_search

Search Product Hunt makers by name, topic, products, or upvotes to retrieve public-profile records for building maker leaderboards.

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.

Input Schema

TableJSON 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
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It states 'Public fields only,' which is a useful constraint, and mentions the dataset id. However, it does not disclose pagination behavior, response format, or that it is a read-only operation, which are typical for a search tool. Some transparency is present, but it is limited.

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

Conciseness5/5

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

The description is concise—three sentences with no filler. It front-loads the primary purpose, then provides the dataset context and sort enum. Every sentence adds value, and it is well-structured for quick parsing.

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

Completeness3/5

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

For a search tool with 7 optional parameters and no output schema, the description provides some context about the data (makers with footprint) and the use case (leaderboards), but it does not explain what the response contains (e.g., list of maker records with pagination). It is adequate for an agent to infer the call, but not fully 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 coverage is 100%, so each parameter has a description. The tool description does not add additional semantics beyond repeating the sort enum values, which are already in the schema. It does provide context that the data includes footprint (products made, total upvotes, topics), but this does not directly clarify parameter usage. The baseline of 3 is appropriate.

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 clearly states the tool's function: 'Search the Product Hunt makers dataset.' It specifies the resource (makers) and the dataset id enum value, and mentions the use case (leaderboards). This differentiates it from sibling tools like datasets_producthunt_makers_item (fetch a specific maker) and datasets_producthunt_makers_facets (get facet counts).

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 description implies this is for searching makers but does not explicitly state when to use it versus alternatives like the item or facets tools. It does not name alternatives or provide exclusion criteria, leaving the agent to infer the appropriate context from the purpose.

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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