Hook Detector

score_package

Score a package against the real outliers on its subject, part by part (title device, length, words on the picture, faces, the words' role, contrast, brightness): each part says how many outliers it matches; pattern_match (0 to 100) is the mean share: how much the package resembles the subject's outliers, never a click-through rate or a prediction (in our test it scored outliers above their channels' usual videos on about 3 of 4 pairs). Also nearest (5 outliers) and fixes. 1 credit, refunded when the read fails. Same as POST /v1/packaging/score.

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": "score_package"
  }
}

Input Schema

Name	Required	Description
`title`	No	your title, 1 to 150 characters (or video_url)
`topic`	No	the subject (the shared index's outliers)
`run_id`	No	a finished run of yours whose outliers to compare with; or topic
`api_key`	No
`thumbnail`	No	optional: your thumbnail as base64 (JPEG, PNG, WebP, at most 4 MB) or an i.ytimg.com URL
`video_url`	No	a published YouTube video to score instead
`content_type`	No	for a topic: long or short
`idempotency_key`	No	1 to 255 visible ASCII characters; the same key and request within 24 hours answer the saved remix and charge nothing; the same key with another request is 422 idempotency_key_reused

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 26, 2026

TDQS

A4.3/5.0

Behavior5/5

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

The description provides immense behavioral context beyond the input schema, detailing the specific analytical sub-metrics used (title device, length, faces, contrast, brightness) and the nature of the scoring mechanism (mean share of pattern_match). Since there are no annotations, this description carries the entire burden, making it exceptionally high quality.

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?

While highly informative, the description is dense and mixes technical scoring jargon with operational caveats (credits, failed reads) and anecdotal evidence ('in our test it scored...'). This density and conversational tone prevent it from achieving a higher score for 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?

The definition is extremely comprehensive. It explains the input context (package), the methodology (part-by-part outlier matching), the core outputs (pattern_match, nearest, fixes), and even the economic risk, providing everything an agent needs to understand and utilize this complex tool.

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?

With schema description coverage at 88%, the schema already defines the purpose of most parameters (e.g., 'title' can be a string or a video_url). The description largely functions to define the *subject* of the score rather than adding deeper meaning to individual parameters, thus adhering to the baseline for high schema coverage.

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 tool's purpose is clearly stated: to score a package against real outliers on its subject. It defines the methodology (part by part analysis) and distinguishes itself by specifying that it measures 'pattern_match' and 'nearest' rather than simple metrics like click-through rate or generic predictions.

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 provides clear usage context by explaining what the output entails (pattern_match, nearest, fixes). Additionally, it provides crucial operational guidance by stating the cost and refund policy ('1 credit, refunded when the read fails'), which helps the agent determine the risk-reward of calling the tool.

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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