add_tiktok_caption

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

With no annotations, the description carries the full burden, and it does disclose one genuinely useful trait: this creates a MOGRT-based title graphic rather than a real caption-track entry, which matters for later get_captions/update_caption calls. It says nothing about prerequisites (active sequence, MOGRT availability), return value, or whether the placement is undoable, so the mutation profile is still thin.

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?

Three short sentences, purpose front-loaded, zero padding or repetition. The third sentence is slightly promotional ('Great for...') but it does carry use-case signal, so it mostly earns its place.

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 an 8-parameter timeline-mutating tool with no annotations and no output schema, the description covers what it makes and roughly why, and the schema covers the parameters. What's missing is disambiguation from sibling text/caption tools and any statement of what the call returns or requires, leaving the agent to guess.

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% and every parameter, including the template enum and the positionX/positionY conventions, is documented in the schema. The description adds only a soft hint about 'big bold text' that loosely maps to the scale parameter, so baseline 3 applies.

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?

States a specific verb and resource ('Add a TikTok-style text caption to the timeline') and even names the mechanism (built-in Premiere Pro title MOGRTs), so the agent knows exactly what gets created. It does not, however, distinguish itself from siblings like add_text_graphic or add_mogrt_to_timeline, which sound like near-equivalents.

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?

'Great for adding big bold text overlays, word highlights, or captions' gives use-case context, which is more than nothing. But there is no when-not guidance and no mention of the obvious alternatives (add_text_graphic, add_caption, add_caption_track), so the agent must infer selection criteria on its own.

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