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datasets_producthunt_trends_search

Search the Product Hunt trends dataset. Returns aggregate Product Hunt launch trends from the dataset id enum value producthunt-trends.

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. 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
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

A4.2/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full behavioral burden and does so: it discloses that output is aggregate-only per category-time cell, that thin cells are suppressed, and it gives precise billing semantics (1 credit per record, minimum 2, empty result costs 2, full 100-row page costs 100). That is exactly the non-obvious operational context an agent needs and cannot get from the schema.

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 purpose and scope, then output shape, then behavior, then billing. Dense and mostly waste-free, though re-listing the group_by and sort enums duplicates the schema and is the one place a sentence does not earn its place.

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?

With no output schema and no annotations, the description still tells the agent what each returned row contains (launch count, total/average upvotes, average rating, top product), that suppression can remove cells, and what the call costs. Nothing needed to interpret results or predict spend is missing.

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 every parameter including enums and the page*page_size <= 10000 cap. The description restates the group_by and sort enum values verbatim without adding syntax, defaults, or edge-case meaning beyond the schema, so the baseline 3 applies.

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?

States a specific verb (Search) and resource (Product Hunt trends dataset) and immediately scopes it: aggregate-only category-over-time cells, never individual product records. That contrast with per-product siblings like datasets_producthunt_products_search is explicit, so an agent can route without opening a schema.

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 when the tool applies (aggregate trend questions) and implies when it does not (anything needing an individual product record), but it never names the alternate tools or states a selection rule. With facets/product siblings present, an explicit 'use X instead for Y' would lift this above the implied-usage baseline.

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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