analyze_videos_batch_start

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

With no annotations, the description carries full burden and delivers: it reveals async nature, cost implications ('50% cheaper'), SLA ('up to 24 hours, typically 15-60 min'), and return structure. It fully discloses behavioral traits beyond the input schema.

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 well-structured with clear sections (use case, args, returns). It is concise but could be slightly trimmed (e.g., the detailed prompt override explanation is useful but slightly verbose). Still, it earns its keeping.

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 has 2 parameters, no output schema, and no annotations, the description is very complete. It covers purpose, usage, behavioral details, and return format. However, it lacks explicit error conditions or failure handling, which is minor.

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 0%, so the description must add meaning. It explains `video_urls` as 'List of full YouTube URLs (https://www.youtube.com/watch?v=...)' and `prompt` as optional override with default behavior and per-batch override mechanism. This adds significant semantic context.

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 'Submit a batch of YouTube videos to Gemini Batch API for async analysis.' It distinguishes from `analyze_video_start` by noting it is async and cheaper. The verb 'submit' and resource 'batch of YouTube videos' are specific, and the context signals show sibling tools with similar names, but the description effectively differentiates.

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?

Explicitly states 'Use this ONLY when the user explicitly says "no rush", "overnight", "do it later", or for scheduled cron digests.' It contrasts with `analyze_video_start` for interactive requests, providing clear when-to-use and when-not-to-use guidance. This is exemplary.

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