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auto_reframe_sequence

Reframe a video sequence to a target aspect ratio with AI motion tracking. Provide sequence ID and ratio values to adapt footage for different platforms automatically.

Instructions

Automatically reframes a sequence to a new aspect ratio using AI-powered motion tracking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
newNameNoName for the reframed sequence
numeratorYesAspect ratio numerator (e.g., 9 for 9:16)
sequenceIdYesThe ID of the sequence to reframe
denominatorYesAspect ratio denominator (e.g., 16 for 9:16)
motionPresetNoMotion tracking speed preset
Install Server

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'AI-powered motion tracking' but does not state whether the operation modifies the original sequence or creates a new one, whether it is reversible, how long it may take, or what the result/return value is. This is a significant transparency gap for a potentially state-changing operation.

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?

The description is a single 12-word sentence with no filler, redundancies, or irrelevant details. The core action and differentiator are front-loaded, making it easy for an agent to parse quickly. It is appropriately sized for the tool's moderate complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, so the description must compensate. It fails to explain what 'reframes' actually does (in-place vs. copy), the role of the optional newName parameter, whether motionPreset affects processing behavior beyond its enum values, or what the tool returns on success/failure. An agent invoking this tool would still have unanswered questions about the operation's outcome and side effects.

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?

Schema description coverage is 100%, so the schema fully documents all five parameters. The description adds no parameter-level detail beyond restating the aspect ratio concept expressed in numerator/denominator, which is already in the schema. Baseline 3 applies because the schema does the heavy lifting; the description adds no extra meaning.

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 description clearly states the specific action ('reframes a sequence'), the resource ('sequence'), and the target ('a new aspect ratio') while adding the differentiator 'using AI-powered motion tracking.' None of the many sibling tools appear to offer this capability, so the purpose is unambiguous and distinguishes it from the rest.

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?

The description implies when to use the tool (when a sequence needs automatic reframing to a different aspect ratio), but it does not provide explicit when-not-to-use conditions, prerequisites, or alternative tool guidance. Because no sibling tool performs reframing, the lack of named alternatives is reasonable, yet the description still leaves usage context to inference rather than stating 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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