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List clips

list_clips
Read-only

The short clips (an AI influencer moving and reacting, no app footage yet) as a grid. With a character id: that influencer's clips, which for a public influencer are the finished ones anyone may reuse plus the user's own; without: the user's own clips, newest first. Use it when the user wants to see clips; to use "my newest clip" in make_video just leave its clip empty, no listing needed. Costs no credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many, default 12.
characterNoAn influencer id from list_characters. Leave empty for the user's own clips across every influencer.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nextYes
itemsYes
characterNoSet when the list is one influencer's.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly, non-destructive, non-openWorld, so the safety profile is covered. The description adds genuinely useful behavioral context: it costs no credits and results are ordered newest first. It does not mention pagination or result count semantics, which is a minor gap.

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 resource definition, then filter semantics, then usage, then cost. Every sentence carries information, though the parenthetical "(an AI influencer moving and reacting, no app footage yet)" is dense and slightly awkward.

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?

An output schema exists so return values need not be explained. With annotations covering safety, both params documented, and filter/cost/ordering behavior described, an agent has everything needed to decide whether and how to call this tool.

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 both params are documented (limit default 12, character from list_characters). The description adds meaning beyond the schema by explaining what the character filter actually returns (public influencer's reusable finished clips plus the user's own) versus the no-character case (own clips across every influencer).

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 (list the short clips) and immediately defines the resource (an AI influencer moving and reacting, no app footage yet). It also differentiates from siblings like list_videos by clarifying these are short clips shown as a grid.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when-to-use ("when the user wants to see clips") and an explicit when-NOT-to-use with an alternative path (for "my newest clip" in make_video, leave clip empty, no listing needed). This routes the agent away from an unnecessary call.

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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