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datasets_producthunt_trends_facets

Facet Product Hunt trends data by topic or launch year to return filtered, suppressed distribution counts with vote and date thresholds.

Instructions

Facet the Product Hunt trends dataset. Returns suppressed distribution counts over the Product Hunt trends dataset (dataset id enum value producthunt-trends), honoring the same filters as search. Facet enum: topic, launch_year.

Input Schema

TableJSON Schema

Name	Required	Description
`facet`	Yes	Facet enum: topic, launch_year
`topic`	No	Exact topic-slug filter, e.g. artificial-intelligence, max 128 characters
`group_by`	No	Aggregate cell dimension enum: topic_month, topic_year, topic. Defaults to topic_month
`min_votes`	No	Minimum product upvotes, 0 or greater
`min_launches`	No	Minimum launches per bucket; raises the small-cell suppression floor
`launched_after`	No	Lower bound on first-launch date, an ISO-8601 date (YYYY-MM-DD)
`launched_before`	No	Upper bound on first-launch date, an ISO-8601 date (YYYY-MM-DD)

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.17.5Sep 26, 2026
- addedInput schema / properties / facet / enum
```
Added value: +[
+  "topic",
+  "launch_year"
+]
```
- addedInput schema / properties / group_by / enum
```
Added value: +[
+  "topic_month",
+  "topic_year",
+  "topic"
+]
```
Addedv1.5.0Jul 14, 2026

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It does disclose a key behavioral trait: the counts are 'suppressed distribution counts', indicating small-cell suppression. However, it does not disclose output shape, aggregation defaults, rate limits, or any side effects, so transparency is only partial.

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 two sentences, front-loaded with the core action, and contains no filler. The repetition of the dataset identifier and facet enum is compact and reinforces key operational details without bloating the text.

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?

Given 7 parameters, no annotations, and no output schema, the description is adequate but minimal. It defines the core operation, dataset, and facet options, but leaves unresolved details such as the exact return structure, default group_by behavior, and how suppression manifests in results. This is a viable definition with clear gaps.

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 baseline is 3. The description does not materially add meaning beyond the schema; it only restates the facet enum, which is already fully documented in the input schema. The 'same filters as search' note is helpful but does not deepen individual parameter understanding.

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 states a specific verb and resource ('Facet the Product Hunt trends dataset') and explains the output ('Returns suppressed distribution counts'), including the dataset id enum value `producthunt-trends` and the allowed facet values. This clearly distinguishes it from sibling search tools like datasets_producthunt_trends_search.

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 phrase 'honoring the same filters as search' implies how the filters should be used and connects it to a sibling search tool, but it does not explicitly state when to prefer facets over search or when not to use this tool. Usage context is implied rather than spelled out.

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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