analyze_video

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

No annotations are provided, so the description carries the full burden. It only states the action and a requirement but does not disclose behavioral traits such as return format, side effects, or limitations (e.g., processing time, file size). This is insufficient for an AI agent to anticipate tool behavior.

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 concise sentences: purpose and prerequisite. No unnecessary words. Front-loaded with the core action.

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?

With no output schema and no annotations, the description lacks critical context about the return value, potential errors, and operational constraints (e.g., supported video formats). The 2-param schema is simple, but behavioral gaps make it incomplete.

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 coverage is 100% (both parameters have schema descriptions). The tool description adds no additional meaning beyond what the schema already provides, so it meets the baseline but does not enhance understanding.

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 what the tool does: 'Analyze video content using a vision language model.' It identifies the specific resource (video) and the method, distinguishing it from sibling tools like analyze_image that handle static images.

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 mentions a prerequisite (model must support video, e.g., Qwen3-VL) but does not explicitly guide when to choose this tool over alternatives like analyze_image or compare_images. The distinction is implied by the resource type (video vs. image).

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