Hook Detector

trending_shorts

Trending Shorts: the short videos (TikToks, Instagram Reels, YouTube Shorts) rising now on a subject, fastest first. Each item has video_id, platform, url, cover_url, creator, caption, plays, posted_at, age_hours, plays_per_hour (plays over the hours since posting, at least one), multiple and multiple_basis (plays over the creator's usual: the median of their latest videos, or TikTok likes per video; null when it could not be measured), creator_usual, duration_s, and spoken_hook with spoken_hook_from (the opening line when one of your own Short form runs delivered the video, or Scribe v2 heard it). At most 20 items, 2 per creator; a video with no post date is never listed. unavailable names a platform whose search did not answer. Free (credits_charged 0); the same subject, platform and window within 30 minutes is served from the cache, and fresh fetches are limited per account per hour (429 rate_limited). 503 search_unavailable when no platform answered. Needs read. Same as GET /v1/shorts/trending.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "trending_shorts"
  }
}

Input Schema

Name	Required	Description
`window`	No	24h, 7d (default) or 30d: how recently they were posted
`api_key`	No
`subject`	No	what the videos are about, 2 to 120 characters
`platform`	No	tiktok, instagram or youtube; leave out for all three

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 26, 2026

TDQS

A3.9/5.0

Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly: it discloses item limits, per-creator caps, exclusion of undated videos, partial-platform failures, free cost, caching, rate limiting, error conditions, and read requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and the opening sentence is efficient, but it becomes dense and includes a long list of return fields even though an output schema exists. That field enumeration is redundant and reduces conciseness.

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's complexity, missing annotations, and available output schema, the description is complete enough for an agent to call it correctly. It covers purpose, constraints, error cases, cost, caching, and authentication needs.

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?

The schema already documents three of four parameters at 75% coverage, including subject, platform, and window. The description adds some useful parameter-related context, such as cache keying by subject/platform/window and platform unavailability, but it does not explain the undocumented api_key parameter or add much beyond the schema.

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 states a specific resource and behavior: short videos trending now on a subject, ordered fastest first, and even points to the underlying endpoint. It is clear what the tool does, but it does not explicitly differentiate itself from sibling tools such as find_hooks or random_outliers.

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 by the subject-based trending-shorts framing, and the description gives useful context about windows, platforms, caching, and rate limits. However, it does not explicitly say when to use this tool versus alternatives or when not to use it.

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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