video_qa

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool asks questions about video content but doesn't describe how it processes videos (e.g., via AI models), potential limitations (e.g., accuracy, video length), or response format. This is a significant gap for a tool with no annotation coverage.

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 front-loaded with a clear purpose statement, followed by concise examples that illustrate usage without unnecessary details. Every sentence earns its place by reinforcing the tool's function, making it 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 and the lack of annotations and output schema, the description is incomplete. It doesn't explain behavioral aspects like how videos are processed, what types of questions are supported, or what the return values might be. This leaves the agent with insufficient context for reliable tool invocation.

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 both parameters ('video_url' and 'question') with clear descriptions. The description adds minimal value beyond the schema by implying the question should be specific and providing examples, but it doesn't elaborate on parameter constraints or usage nuances. Baseline 3 is appropriate given the high schema coverage.

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 purpose as 'Ask a specific question about a video's content,' which includes a verb ('Ask') and resource ('video's content'). It distinguishes from siblings like 'summarize_video' or 'analyze_video' by focusing on Q&A rather than summarization or general analysis. However, it could be more specific about the type of questions supported beyond the examples.

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 implies usage through examples (e.g., 'How many people appear?'), suggesting it's for specific, factual questions about visual content. It doesn't explicitly state when to use this tool versus alternatives like 'analyze_video' or 'extract_video_text,' nor does it provide exclusions or prerequisites, leaving some ambiguity for the agent.

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