vision_analyze_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 reveals the URL-only restriction, the effect of thinking=True, and the exact return JSON structure including both success and error formats. Though it omits details like rate limits or video size constraints, it covers the key behavioral aspects for this tool's scope.

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 neatly structured: a one-line purpose statement followed by specific input constraints, parameter definitions, and return format. Each sentence adds meaningful information without redundancy, making it concise and efficiently 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?

For a tool with no annotations and a sparse schema, the description covers all necessary context: purpose, usage constraints, parameter semantics, and return format. The output schema exists but the description even summarizes the response shape, leaving no significant gaps for an agent to invoke the tool 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?

The schema has zero description coverage, so the description fully compensates by explaining every parameter: source's URL requirement and handling of local files, question's role, and thinking's boolean toggle meaning. This exceeds what the schema provides and clarifies how to use each argument correctly.

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 tool's function: 'Analyze a video and answer questions.' This uses a specific verb and resource, and naturally distinguishes it from sibling tools like vision_analyze_image and vision_analyze_file by focusing on video input.

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 provides explicit context on usage prerequisites: source must be a publicly accessible URL and local videos must be uploaded first. While it doesn't explicitly compare to alternatives or offer exclusion criteria, the video-specific purpose and constraints give clear operational guidance.

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