analyze_audio_video

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

No annotations are provided, so the description must carry the full burden. It specifies the model used and the method for handling local files (base64 with 25MB guardrail). However, it does not disclose whether the operation is read-only, rate limits, or permissions required.

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 long, front-loaded with the primary purpose, and efficiently covers the input format. Every word earns its place.

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?

There is no output schema, so the description should hint at the return format. It only mentions the model and analysis capability. It does not state that the output is a textual analysis, which leaves a gap in completeness.

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% with descriptions for all three parameters. The description adds value by explaining how video_url is processed (base64 data URL for local files, 25MB limit), which is not in the schema. No additional detail for question or max_tokens.

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 that the tool analyzes a video including its audio track using a specific model (Qwen3.5-Omni). It distinguishes itself from sibling tools like analyze_video and analyze_audio by explicitly covering both modalities.

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 explains when to use the tool (for video + audio analysis) and how to provide input (public URL or local file path with base64 encoding). It does not explicitly state when not to use, but sibling tool names suggest alternatives.

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