apply_effect

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'apply' implies a mutation operation, the description doesn't specify whether this overwrites existing effects, requires specific permissions, has side effects on layer properties, or what happens on success/failure. For a mutation tool with zero annotation coverage, this leaves critical behavioral aspects undocumented.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at just five words, front-loading the core action and target without any wasted words. Every element ('apply', 'an effect', 'to a layer') contributes essential information about the tool's function. This represents optimal efficiency in description length.

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

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

For a mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what happens after application (success indicators, error conditions, return values), doesn't provide usage context relative to sibling tools, and offers minimal behavioral transparency. The combination of mutation operation with poor contextual support creates significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with all three parameters clearly documented in the input schema (compId, layerIndex, effectName). The description adds no additional parameter information beyond what's already in the structured schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('apply') and target ('to a layer'), specifying the resource (effect) being applied. It distinguishes from siblings like 'apply_animation_preset' or 'apply_expression_preset' by focusing specifically on visual/processing effects rather than animation or expression presets. However, it doesn't explicitly differentiate from all possible effect-related operations that might exist in other contexts.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing composition and layer), when not to use it, or what alternatives exist for similar operations. Given the sibling tools include various 'add_' operations for different layer types and effects, this lack of contextual guidance is a significant gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.