analyze_video

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

No annotations are provided, so the description must disclose behavioral traits. It only mentions model requirements, omitting details like processing speed, output format, potential errors (e.g., unsupported video formats), or whether videos are processed entirely. The agent lacks critical behavioral context.

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 two sentences, front-loading the primary action and then a key requirement. Every word is purposeful; no redundancy.

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 tool's simplicity (two parameters, no output schema), the description is adequate but could be improved by stating what the output is (e.g., returns text) and any limitations (e.g., video length). It leaves some context gaps.

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 input schema describes both parameters (video and prompt) with clear documentation, covering 100% of properties. The description does not add additional semantics beyond the schema, so a baseline score of 3 is appropriate.

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 analyzes video content using a vision language model. It implicitly distinguishes from sibling tools like analyze_image (images) and OCR (text in images) by specifying video support. However, it lacks explicit mention of the analysis type beyond general AI interpretation.

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 notes the requirement for a model with video support, implying conditions for use, but gives no explicit guidance on when to use this tool versus siblings (e.g., vs analyze_image for static frames). The context of sibling names provides some implicit differentiation, but the description does not state when-not-to-use or alternatives.

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