submit_video

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the asynchronous nature (returns immediately with job_id), typical processing time (30-120 seconds), polling mechanism (call check_video), output location (written to OUTPUT_DIR), and format (MP4). This covers execution flow, timing, and output handling beyond basic functionality.

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 appropriately sized and well-structured: it starts with the core purpose and immediate return, then explains the asynchronous workflow, and finally details each parameter in a clear Args section. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 complexity of an asynchronous video generation tool with 6 parameters, no annotations, and an output schema (implied by 'Returns immediately with a job_id'), the description is complete. It covers the tool's purpose, behavioral workflow, parameter semantics, and integration with sibling tools, providing all necessary context for an agent to use it correctly.

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 fully compensate. It provides detailed semantic explanations for all 6 parameters: purpose of prompt, model options with cost/quality trade-offs, aspect ratio meanings, resolution options with constraints (4k rejected for lite), optional negative_prompt usage, and image_path for image-to-video. This adds substantial meaning beyond the bare schema.

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 specific action ('Submit a Veo 3.1 video generation job') and resource ('video'), distinguishing it from siblings like check_video (polling), generate_image (image creation), and list_videos (listing). It uses precise verbs and identifies the exact resource being created.

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 provides clear context for when to use this tool (to start a video generation job) and explicitly mentions check_video as the follow-up for polling completion. However, it doesn't explicitly state when NOT to use it or compare it to alternatives like generate_image for image generation, leaving some sibling differentiation implicit.

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