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ae_batch_apply_effect

Apply an effect to all layers in a composition, or filter by layer type to target specific layers. Skip locked layers and batch update multiple layers in one action.

Instructions

Apply an effect to all layers (or filtered by type) in a composition

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
comp_nameYesComposition name
skip_lockedNoSkip locked layers
effect_match_nameYesEffect match name (e.g. 'ADBE Gaussian Blur 2')
layer_type_filterNoOnly apply to layers of this typeall

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It only states that an effect is applied to layers and does not mention that this is a wide-reaching mutation, whether existing effects are preserved, how locked layers are handled, or what happens when no layers match the filter.

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, front-loaded sentence with no filler. Every word contributes to stating the operation and its scope.

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?

For a batch mutation tool with no annotations and no output schema, the description omits important operational context: the potential for broad changes across a composition, no mention of success/failure behavior, and no guidance about the effect_match_name requirement being non-trivial. Schema coverage helps parameter semantics but does not fill this contextual gap.

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 all four parameters are already documented in the input schema. The description only adds a passing reference to type filtering, which does not substantially improve parameter understanding beyond the schema.

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 (apply an effect) and a specific scope (all layers, optionally filtered by type) within a composition. This clearly differentiates the tool from sibling ae_apply_effect, which targets a single layer.

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 a batch use case, but it does not explicitly say when to choose this over ae_apply_effect or other sibling tools. The layer_type_filter parameter hints at usage scenarios, but no explicit when/when-not guidance is provided.

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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