video_analysis

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

With no annotations, the description carries the full burden and does well: it discloses that the tool returns text, analyzes inline, may reject large files, supports dry_run to avoid spending, and describes the kind of information extracted. This is rich, non-obvious 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 efficiently structured: front-loaded with the core action and return type, followed by usage context and practical caveats. Each sentence adds meaningful information without repetition.

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?

Despite no output schema and no annotations, the description covers input types, prompt behavior, failure mode (large files), dry-run option, and intended use relative to generation workflows. It is a complete and self-sufficient description for a relatively simple tool.

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%, so the baseline is 3. The description adds value by explaining how the prompt affects output (specific vs. general), what dry_run does, and reaffirming video source formats, going slightly beyond bare schema names.

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 watches a video and answers a question, explicitly noting it returns text rather than a video. It distinguishes itself from video_generate and video_stitch by framing analysis as a pre-generation reading step.

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 tells when to use it ('before generating'), how to customize with a prompt, and how to handle large files. It doesn't explicitly name alternative tools or when not to use it, so it lacks full exclusion guidance.

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