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datasets_producthunt_trends_search

Search aggregated Product Hunt launch trends by topic, time period, and upvote thresholds. Returns launch counts, average votes, ratings, and top products per cell.

Instructions

Search the Product Hunt trends dataset. Returns aggregate Product Hunt launch trends from the dataset id enum value producthunt-trends. Aggregate-only: each row is a category-over-time cell (a topic, optionally within a calendar period), reporting launch count, total and average upvotes, average rating and the top product — never an individual product record. Thin cells are suppressed. group_by enum: topic_month, topic_year, topic. Sort enum: period_desc, period_asc, launch_count_desc, sum_votes_desc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, defaults to 1
sortNoSort enum: period_desc, period_asc, launch_count_desc, sum_votes_desc
topicNoExact topic-slug filter, e.g. artificial-intelligence, max 128 characters
group_byNoAggregate cell dimension enum: topic_month, topic_year, topic. Defaults to topic_month
min_votesNoMinimum product upvotes, 0 or greater
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
min_launchesNoMinimum launches per cell; raises the small-cell suppression floor (never lowered below the built-in minimum)
launched_afterNoLower bound on first-launch date, an ISO-8601 date (YYYY-MM-DD)
launched_beforeNoUpper bound on first-launch date, an ISO-8601 date (YYYY-MM-DD)
Behavior4/5

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

With no annotations provided, the description discloses key behaviors: output is aggregate per category-over-time cell, thin cells are suppressed, and the returned fields (launch count, upvotes, rating, top product). It does not mention rate limits or authentication, but covers the essential behavioral aspects for correct usage.

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 yet comprehensive, with clear front-loading of the main purpose. Each sentence adds value: dataset identification, aggregate nature, row structure, suppression behavior, and parameter enums. No unnecessary repetition.

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?

The description fully explains the tool's output (aggregate cells with specific metrics) despite no output schema. It covers the dataset, suppression, grouping, sorting, and parameter effects. For a search tool with 9 parameters, it provides sufficient context for accurate use.

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 description coverage is 100%, so baseline is 3. The description adds value by explaining the meaning of enums (group_by, sort) and the effect of parameters like min_launches (raises suppression floor). It goes beyond the schema by clarifying the aggregate cell dimensions and sort options.

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 it searches the Product Hunt trends dataset, specifying the dataset ID and that it returns aggregate launch trends (counts, upvotes, rating, top product) rather than individual products. It distinguishes itself from sibling producthunt tools (e.g., datasets_producthunt_products_search) by emphasizing aggregate-only data.

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?

The description explicitly states 'Aggregate-only' and that it never returns individual product records, guiding the AI to use it for trend analysis. It explains grouping and sorting enums. However, it does not explicitly contrast with siblings like datasets_producthunt_trends_facets or provide when-not-to-use, missing some contextual guidance.

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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