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datasets_producthunt_trends_search

Search the Product Hunt trends dataset to analyze aggregate launch metrics by topic and period, including upvotes, ratings, and top products.

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)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / group_by / enum
      Added value: +[
+  "topic_month",
+  "topic_year",
+  "topic"
+]
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "period_desc",
+  "period_asc",
+  "launch_count_desc",
+  "sum_votes_desc"
+]
  2. Addedv1.5.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that rows are aggregate-only, that thin cells are suppressed, and that each cell reports launch count, upvotes, rating, and top product. It does not cover every edge case like error behavior or rate limits, but the core behavioral traits are well described.

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 dense but efficient, front-loading the core purpose and aggregate-only behavior before detailing row structure and enums. Every sentence contributes useful information, and there is no filler or repetition of schema content. The structure makes the most important constraints immediately visible.

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?

Given the tool has 9 parameters and no output schema, the description provides a remarkably complete picture: dataset identity, aggregate row shape, suppression behavior, grouping options, sort options, and filter semantics. An agent has enough information to call the tool correctly even with no prior knowledge. The only minor omission is an explicit note on response pagination wrapping, but the schema already covers page and page_size constraints.

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 the baseline is 3. The description adds meaningful context beyond the schema by explaining that rows are category-over-time cells, that min_launches raises the suppression floor, and by framing group_by and sort enums in terms of aggregate trend semantics. This elevates the parameter guidance above bare schema definitions.

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 searches the Product Hunt trends dataset and returns aggregate launch trends, with a specific verb and resource. It further distinguishes itself from individual product retrieval by explicitly stating each row is a category-over-time cell and never an individual product record. This makes the tool's purpose unambiguous and separable from sibling tools.

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 clearly indicates this tool is for aggregate Product Hunt launch trend analysis rather than individual product lookup. It explains the aggregate-only nature and the meaning of grouping, but it does not explicitly name alternative tools or state 'use X instead' for individual products. Still, the usage context is clear enough for an agent to select it appropriately.

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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