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datasets_producthunt_makers_search

Search Product Hunt makers by name, topic, or sort by votes, products, followers to find maker profiles with their product footprint and rankings.

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
Behavior3/5

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

The description implies a read-only search via 'Search' and 'public fields only', but does not disclose behavioral traits such as rate limits, pagination behavior, or whether the tool is destructive. With no annotations provided, the description fails to fully carry the burden of transparency.

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 three sentences, front-loaded with the core purpose and dataset ID, followed by a clear explanation of what makers are and key details. No unnecessary words; each sentence earns its place.

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?

The description lacks context on return format, pagination limits (though in schema), and when to use this vs. sibling tools. Given the detailed schema, it is adequate but not fully complete for an agent to confidently select and invoke the tool without additional hints.

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 is already described in the input schema. The description adds minimal extra value by explicitly listing sort enum values and hinting that 'q' covers name and headline. This is adequate but does not significantly enhance understanding of parameters like min_products or min_total_votes.

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?

The description clearly states it searches Product Hunt makers from a specific dataset, differentiating it from other producthunt tools that search products or live API. However, it does not explicitly distinguish from the sibling tool 'producthunt_makers' which may serve a similar function but without the dataset context.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like 'datasets_producthunt_makers_facets' or 'producthunt_makers'. The statement 'Public fields only' hints at limitations but does not provide explicit when-to-use or when-not-to-use context.

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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