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add_nla_strip

Add an animation clip to a specified NLA track on an object, with control over start frame, scale, repeat, and blend mode.

Instructions

Add an animation clip (strip) to an NLA track.

Args: object_name: Target object. track_name: NLA track to add the strip to. action_name: Action (animation clip) to use. name: Custom name for the strip. start_frame: Frame where the strip starts. scale: Time scale for the strip. repeat: Number of times to repeat. blend_type: REPLACE, ADD, SUBTRACT, MULTIPLY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
scaleNo
repeatNo
blend_typeNoREPLACE
track_nameYes
action_nameYes
object_nameYes
start_frameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It only states the action and parameter meanings, but does not disclose requirements like existing track/object, error behavior, or whether the operation is reversible. It also doesn't mention if the strip replaces existing strips or any side effects.

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 description is concise, with a one-line purpose and a parameter list. It avoids unnecessary fluff and is easy to scan. However, the structure is a simple docstring without highlighting important prerequisites or notes.

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?

The tool has 8 parameters, no output schema, and no annotations. The description only covers the basic operation and parameter meanings, omitting any context on expected inputs (e.g., that the track must exist), return values, or error handling. An agent would need more guidance to use this tool reliably.

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 0%, so the description must compensate. It provides brief descriptions for all 8 parameters, such as 'object_name: Target object' and 'blend_type: REPLACE, ADD, SUBTRACT, MULTIPLY.' However, these are minimal and lack details on constraints or default behaviors beyond the schema's defaults.

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 action: 'Add an animation clip (strip) to an NLA track.' This clearly identifies the verb and resource, and distinguishes it from sibling add_nla_track by specifying the strip addition to an existing track.

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

Usage Guidelines2/5

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

The description does not mention any conditions for when to use this tool versus alternatives. It lacks exclusions or references to other tools, leaving the agent without guidance on selecting it over related NLA operations.

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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