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producthunt_customers

Retrieve the customer products of a Product Hunt product by slug. Supports pagination and ordering by customers or latest launch.

Instructions

Retrieve Product Hunt product customers. Lists the customer products of a Product Hunt product, looked up by its slug in id. order is customers (default) or latest_launch, page defaults to 1 and page_size to 10. Each item has its name, categories, badges, review and follower counts, and founder shoutouts. An unknown slug returns 404.

Input Schema

NameRequiredDescriptionDefault
idYesProduct Hunt slug
pageNoPage number
orderNoProduct Hunt customers order
page_sizeNoResults per page

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / order / enum
      Added value: +[
      +  "customers",
      +  "latest_launch"
      +]
  2. First observedv1.0.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does a solid job: it discloses defaults (order=customers, page=1, page_size=10), the per-item payload (name, categories, badges, review/follower counts, founder shoutouts), and the 404 behavior for an unknown slug. It does not state read-only safety, rate limits, or result-set size, leaving some behavioral gaps for a mutation-free but undocumented read.

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 the core purpose, then defaults, then return shape, then the error case – a sensible ordering. There is minor redundancy between 'Retrieve Product Hunt product customers' and 'Lists the customer products of a Product Hunt product', but no sentence is wasted overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description correctly compensates by enumerating the fields of each returned item, and it covers the required slug plus all three optional pagination/ordering parameters with defaults. Complete enough for correct invocation; only the absence of any safety/read hint keeps it from the top.

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 coverage is 100%, so the baseline is 3, but the description adds real meaning the schema lacks: the enum's default value ('customers' default) and the default values for page (1) and page_size (10), which the schema describes only as 'Page number' and 'Results per page'. That is genuine added value over the structured fields.

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 gives a specific verb and resource ('Retrieve Product Hunt product customers', 'Lists the customer products of a Product Hunt product, looked up by its slug in id'), so an agent knows exactly what it returns and what input selects it. It never names or contrasts with siblings like producthunt_product, producthunt_makers, or producthunt_alternatives, which is the only thing keeping it from a 5.

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?

Usage is implied rather than stated: the agent learns a Product Hunt slug is required and that an unknown slug returns 404, which signals precondition and failure mode. There is no explicit when-to-use/when-not guidance or routing to alternative tools, so the agent must infer the appropriate 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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