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Most popular APIs in a category

most_popular
Read-only

Call this when the user asks what other people use, or wants a safe, proven choice: the 3 APIs in a category with the most paying users in the last 30 days, "Most popular" first. APIs with warnings are held back and named. Category ids: web-search, web-extraction, llm-inference, crypto-prices, onchain-data, enrichment, weather, news, social-media, jobs, ecommerce, maps-places, translation, messaging, travel, stocks, pdf-generation, captcha-solving, sports-data. Free, read-only and quick: it never calls or pays the APIs it describes.

Input Schema

NameRequiredDescriptionDefault
railNoPaid rails to include, e.g. ["x402"] for pay-per-call only (default both)
taskNoSub-category task ids, any of, e.g. ["comments"]. See list_categories
recentNotrue = only endpoints checked in the last 7 days
categoryYesCategory id, e.g. 'web-search'
platformNoSub-category platform ids, any of, e.g. ["instagram","tiktok"]. See list_categories
hide_flagsNoWarnings to leave out, e.g. ["wallet-mismatch", "few-repeat-buyers"]
max_price_usdNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations, it discloses cost ('free'), latency ('quick'), and a key non-obvious behavior: it never calls or pays the APIs it describes. It also explains that flagged APIs are held back and named rather than silently dropped, and pins the ranking window to 30 days.

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?

The trigger and ranking rule are front-loaded, and the closing cost/read-only sentence earns its place. The 19-item category enumeration is useful but bulky and the middle sentences run long, making it denser than ideal.

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?

For a 7-parameter, 1-required read tool with no output schema, it covers ranking definition, ordering, flag handling, cost, and valid category values. It does not describe what each returned entry contains or how the filter parameters reshape the result set, leaving a modest gap.

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 86%, so the baseline is 3, and the description earns an upgrade by enumerating the valid category ids, which the schema only illustrates with a single example. It also ties hide_flags to the 'warnings held back and named' behavior, though rail, task, platform, recent, and max_price_usd go unmentioned.

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 names a specific resource and ranking rule: the 3 APIs in a category with the most paying users in the last 30 days, ordered 'Most popular' first. That is concrete enough to separate it from find_best or compare_category in practice, but it never names those siblings, so the differentiation is inferred rather than stated.

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?

It gives an explicit trigger ('when the user asks what other people use, or wants a safe, proven choice'), which is a clear usage context. It offers no when-not guidance and no named alternative for cases like price comparison or single-endpoint inspection, which the sibling tools would cover.

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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