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

Annotations already mark this as non-read-only and destructive, and the description adds important behavioral context by disclosing that it 'spends credits,' is hidden from host LLMs, and returns a generation_id for later polling. This meaningfully supplements the annotation hints, though it does not detail consequences beyond credit spend.

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 sentences deliver the key facts: what it does, who should call it, and what it returns. Every sentence earns its place, and the most critical constraint ('App-only') is front-loaded.

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 9-parameter schema with full coverage and no output schema, the description covers the essential usage context: app-only, credit cost, caller, and returned generation_id. It is nearly complete, though it slightly undersells the one-source-input constraint among image_url, uploaded_file_id, and generation_result_id, which is only implied per-parameter.

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%, so each parameter is already documented in the input schema. The description does not add parameter-level meaning beyond noting the image-to-video nature, which is adequately covered by the schema. 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?

Description opens with a specific verb and resource: 'queue an image-to-video generation for a montage clip.' It further differentiates itself from siblings by noting it is app-only, hidden from host LLMs, and tied to a montage editor Generate action, so an agent can clearly tell it apart from other generation or montage tools.

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 explicitly states when this tool is used: 'the montage editor calls this when the user presses Generate on a draft clip.' It also gives an exclusion by saying it is hidden from host LLMs and names the follow-up tool, get_generation_status, for polling. This is strong usage guidance with minimal inference required.

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