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 adds useful context about the process (download video, extract frames, analyze frames) and constraints (video must be publicly accessible). However, it lacks details on rate limits, authentication needs, error handling, or response format, which are important for a tool with potential computational and network implications.

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 front-loaded, starting with the core purpose. Each sentence earns its place: the first states the action, the second specifies a key constraint, the third outlines the process steps, and the examples illustrate usage without redundancy. It's efficient and well-structured for quick understanding.

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 video analysis with 4 parameters, no annotations, and no output schema, the description is mostly complete. It covers the purpose, process, constraints, and usage examples, but lacks details on behavioral aspects like performance, limitations, or output format, which would be helpful for an AI agent to manage expectations and errors.

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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the purpose of parameters through examples (e.g., question examples relate to the 'question' parameter), but it doesn't provide additional syntax, format, or usage details. This meets the baseline of 3 when schema coverage is high.

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 ('analyze a video using Qwen3-VL-8B vision-language model on Blaxel') and distinguishes it from siblings by focusing on comprehensive video analysis rather than text extraction (extract_video_text), simple Q&A (video_qa), or summarization (summarize_video). It specifies the resource (video) and method (vision-language model).

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 (analyzing video content with questions) through examples like 'What happens in this video?' and 'Summarize the main events.' However, it doesn't explicitly state when not to use it or name specific alternatives among siblings, such as when to choose summarize_video instead for pure summarization without custom questions.

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