get_shot_prompts

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

With no annotations, the description carries the full behavioral burden. It explains that the tool returns only the body layer, that asset_tokens must be preserved or reference images will drift, that the platform appends additional instructions during generation, and that changing prompts does not auto-regenerate visuals. These are non-obvious behaviors disclosed clearly.

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 dense but every sentence adds operational value: resource, workflow, alternative, token-preservation warning, generation-context warning, and post-edit behavior. The most critical scoping information 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 no annotations and no output schema, the description still equips the agent with the return content (image_prompt, video_prompt, asset_tokens), the required next action (update_shot), and the regeneration consequence. Nothing essential to invoking the tool correctly is missing.

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?

The sole parameter storyboard_id is not formally documented in the description, but '某一镜' and '改哪镜读哪镜' clearly indicate it is the shot identifier to read. This compensates for the 0% schema coverage; only a literal parameter-by-parameter mapping is missing.

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 identifies a specific verb ('读' read), a precise resource (the two prompt bodies of one shot: image_prompt and video_prompt), and clarifies how it differs from get_storyboards, which lacks prompt bodies. This leaves no ambiguity about what the tool is for.

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?

It gives explicit when-to-use guidance ('改哪镜读哪镜'), identifies the intended workflow (read then write back with update_shot), and names an alternative (get_storyboards) while explaining why it is not the right choice for prompt content. This is model-level usage guidance.

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