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Suggest hook texts

list_hooks
Read-only

Fetch AI-suggested hook lines for UGC videos; supply app context to tailor them, then choose one to pass as the hook to merge_ad. Costs no credits.

Instructions

Get AI-suggested hook texts (the short attention-grabbing line burned over a UGC video). Same as clipugc hooks suggest. Pass context describing the app for tailored hooks. Pick one (max 150 chars) and pass it as hook to merge_ad. Costs no credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoDescribe the app, e.g. "my app is a habit tracker" (same as --context).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds genuinely useful behavioral facts the annotations don't: the call costs no credits and the returned hook is capped at 150 characters. It stops short of describing how many hooks come back or their ordering.

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?

Three short sentences, front-loaded with the purpose, then the equivalence to the CLI command, then the usage chain and cost. No filler and nothing buried.

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 single-optional-param, no-output-schema tool, the description covers what is returned (hook texts, 150-char limit), that it's free, and how to consume the result. Missing only the cardinality/shape of the returned list, which an output schema would normally supply.

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?

Only one optional parameter exists and schema description coverage is 100%, so the schema already documents `context` fully. The description's 'Pass context describing the app for tailored hooks' explains the payoff of providing it, which is a mild addition beyond the schema's example, but nothing about format or length limits is added.

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?

States a specific verb+resource ('Get AI-suggested hook texts') and parenthetically defines what a hook is, so an agent unfamiliar with the domain can still tell this apart from siblings like list_images or list_characters. It also ties the tool to a concrete downstream artifact (the line burned over a UGC video).

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?

Gives a clear when-to-use path: pass context to get tailored hooks, then pick one and hand it to merge_ad as the `hook` argument. That routes the agent through the intended workflow, though it doesn't state when this tool should NOT be used (e.g. if an existing hook is already chosen).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.