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set_anim_number_of_frames

DestructiveIdempotent

Set the frame count of a UAnimSequence's data model in one undo step and save the package. Use it to fix or update animation length; the new count is read back to confirm the change.

Instructions

Set the number of frames of the data model of a UAnimSequence (IAnimationDataController::SetNumberOfFrames, one undo step), then save the package; frame_count is read back from the data model. When the data model rate differs from the sequence sampling rate (as after set_anim_frame_rate), a count that is not a whole number of frames at the sampling rate is an error naming the multiple to use, with nothing changed (only the default sampling rate is checked, not per-platform overrides of it). The current count gives saved=false and a note, with nothing saved. A count the data controller rejects is an error with nothing saved, and a failed save makes success false, with saved=false and save_error.

Returns: { sequence_path, frame_count, saved, save_error, note? }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
frame_countYesNew number of frames (integer > 0).
sequence_pathYesAsset path of the UAnimSequence.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations declare a destructive, non-read-only, idempotent write, and the description goes well beyond them: it discloses the single undo step, the save-the-package side effect, the specific error when a count is not a whole number of frames at the sampling rate (naming the multiple to use), the caveat that only the default sampling rate is checked, and the no-op note behavior for the current count.

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?

The action and its primary effect are front-loaded in the first sentence, with edge cases following. It is dense and slightly long, but every clause carries distinct behavioral information relevant to correct invocation, so little is wasted.

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?

Although there is no output schema, the description fully specifies the return shape ({ sequence_path, frame_count, saved, save_error, note? }) and covers the relevant failure and no-op paths. Nothing an agent needs to call and interpret this tool is missing.

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 the baseline is 3, but the description adds real meaning: frame_count is read back from the data model, and an invalid count is rejected as an error unless it is a whole number of frames at the sampling rate. This clarifies the value constraints beyond the schema's 'integer > 0'.

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 states a specific verb and resource: 'Set the number of frames of the data model of a UAnimSequence', and names the underlying API (IAnimationDataController::SetNumberOfFrames). This clearly distinguishes it from siblings like set_anim_frame_rate and set_sequence_duration.

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 gives strong situational context: the relationship with set_anim_frame_rate is named, and it explains what happens when the data model rate differs from the sampling rate. However, it never states an explicit 'use this instead of X when...' routing rule, so usage is well-contextualized but not fully prescriptive.

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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