List the account's brand kits (logo, colors, fonts, motion style). Returns brand_ref ids to pass as brand_kit_id in create_auto_video to produce an on-brand video.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description must cover behavior. It states the tool lists brand kits and returns brand_ref ids. However, it omits details like authentication, output structure, or whether multiple kits are returned, leaving some gaps.
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?
Two efficient sentences with no wasted words. Front-loads the action and immediately clarifies purpose and usage.
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 simple no-parameter list tool, the description covers the main action and usage. Missing explicit output format (array vs object) but adequate for agent context.
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?
Zero parameters; schema coverage is 100% trivially. Baseline of 4 applies. Description adds value by explaining that the returned ids are used in create_auto_video.
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?
Clearly states 'List the account's brand kits' with specific resources (logo, colors, fonts, motion style) and connects to create_auto_video, distinguishing it from sibling list 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?
Explains the output is for use in create_auto_video, giving clear context for when to invoke. Lacks explicit exclusions or alternative comparisons, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clear, distinct purpose. There is no overlap between tools like check_account and check_configuration, or between the two create video modes. All list tools target different entities, and the remaining tools serve unique roles (estimate credits, get video, wait for video, render preview).
All tool names follow a consistent verb_noun pattern in snake_case, such as check_account, create_auto_video, list_models. This makes it easy to predict tool names and understand their purpose.
With 13 tools, the number is well-scoped for a video generation API. It covers setup, account management, creation (two modes), credit estimation, retrieval, listing of various resources, and video waiting. No tool feels redundant or missing.
The tool surface covers the core workflow well: configuration check, account info, video creation (both auto and manual), credit estimation, retrieval, and listing. However, there are no tools for updating or deleting videos, nor for managing resources beyond listing (e.g., creating avatars or brand kits). These are minor gaps.
