animation_sanitize

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

No annotations are provided, so the description carries full burden. It mentions that fixes can be applied optionally and returns JSON, but it does not disclose whether the tool is destructive, requires authentication, or its idempotency. Behavioral traits are minimally conveyed.

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 concise and front-loaded with the primary purpose. It includes necessary format examples without excessive verbosity. However, it could be better structured with bullet points or grouping.

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?

Given the tool's complexity (15 parameters, no output schema), the description lacks completeness. It does not explain the function of many parameters, return value details, or error handling. Significant gaps remain for effective use.

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 0%, so the description must compensate. It only explains three parameters (layer_frame_ranges, out_of_range_action, ignore_full_canvas_overlaps) with format examples, leaving 12 parameters undocumented. This is insufficient for a tool with 15 parameters.

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 tool's purpose: 'Normalize animation consistency and optionally apply fixes.' It provides specific examples of parameter formats and mentions that it returns JSON for AI consumption. However, it does not explicitly differentiate from sibling tools like audit_animation or validate_scene.

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 implies usage for checking and fixing animation consistency, but it does not provide explicit guidance on when to use this tool versus alternatives. No when-not-to-use or exclusion criteria are given.

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