prepare_note_material

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

With no annotations provided, the description carries the disclosure burden. It discloses the async behavior (returns PENDING), the optional frame extraction with file:// paths, platform-specific behavior (Bilibili-only comments), and default off for frame extraction. It also clearly states no LLM is invoked. It lacks details on error states or rate limits, but for this tool the coverage is substantially above average.

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 relatively long but well-structured with bullet points that map to parameters. Every sentence provides useful information, including intended usage, defaults, and result format. It is not overly verbose despite covering many optional parameters, though it could be slightly more concise by omitting the result fields since an output schema exists.

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?

Even with an output schema present, the description details the return shape ({task_id, status: PENDING} and SUCCESS result fields) which is helpful for understanding the async flow. It also mentions the follow-up tools, making the workflow clear. Minor gaps: no mention of failure/error responses, but overall context is sufficient for an agent to use it correctly.

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?

Despite 0% schema description coverage, the description manually explains every parameter: video_url (required), platform (auto-detect), video_understanding/video_interval/grid_size (frame extraction semantics with defaults), and include_comments/comments_limit (Bilibili-only, default 20). It adds meaning to defaults and conditions, fully compensating for the schema lacking descriptions.

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 function: it submits a video link/local file and asynchronously produces a material package (transcript, frames, comments, paths). It distinguishes itself from generate_note by explicitly noting it does NOT call LLM and is for AGENT to read material. This makes the purpose unambiguous and differentiates it from sibling tools.

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 explicitly says '需要 AI 生成结构化 Markdown 笔记请用 generate_note' (use generate_note for AI-generated notes), clearly directing to the alternative. It also instructs to use get_task_status / wait_for_note for querying results, and mentions that no LLM provider/model configuration is needed. This provides strong when-to-use and follow-up guidance.

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