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generate_note

Convert a video link or local file into AI-generated Markdown notes. Starts an async job, returns a task ID, and produces a portable note with optional screenshots and formats.

Instructions

提交一个视频链接/本地文件,异步生成 AI Markdown 笔记。

- video_url: 必填,B 站/YouTube/抖音/快手链接或本地文件路径;
- platform: 可省略,自动识别;
- quality: fast / medium / slow;
- provider_id: LLM 供应商 id(先 list_providers 查看,add_provider 新增);
- model_name: 省略时取已配置的默认模型(setup 向导设置),否则取该供应商第一个可用模型;
- format: 附加内容,如 ["toc","link","screenshot","summary"];
- style: 输出风格(minimal 精简/detailed 详细/academic 学术/tutorial 教程/xiaohongshu 小红书/life_journal 生活向/task_oriented 任务导向/business 商业风格/meeting_minutes 会议纪要);不传时用 setup ③ 配置的默认(默认 detailed);显式传入始终覆盖;
- extras: 附加到 prompt 末尾的自定义指令(如自定义笔记风格要求);内置风格用 style,自定义风格用 extras;
- include_comments / comments_limit: 是否抓取 B 站弹幕+热门评论作为参考注入 prompt(仅 B 站视频生效);不传时用 setup 默认(默认关 / 20 条);显式传入始终覆盖;
- video_understanding / video_interval / grid_size: 视频理解(需多模态模型);不传时用 setup ③ 配置的默认(默认关 / 6s);显式传入始终覆盖;
- screenshot + format 含 "screenshot": 插入图片,产出便携笔记 note.md + Assets/(相对引用);不传时用 setup ③ 配置的默认(默认关);显式传入始终覆盖;
- notes_dir: 便携笔记的输出目录(可选;缺省 BILINOTE_NOTES_DIR 环境变量,再缺省 note_results/{task_id}/)。

返回 {task_id, status, platform}。之后用 get_task_status / wait_for_note 查询结果;
SUCCESS 时 result.note_dir 指向便携笔记目录。

只需素材(转写/帧/评论,不调 LLM 总结)供自行写笔记时,用 prepare_note_material。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linkNo
styleNo
extrasNo
formatNo
qualityNomedium
platformNo
grid_sizeNo
notes_dirNo
video_urlYes
model_nameNo
screenshotNo
provider_idNo
comments_limitNo
video_intervalNo
include_commentsNo
video_understandingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully discloses the tool's behavior: async processing, return format ({task_id, status, platform}), file output (portable notes with Assets/ folder), default fallback chains (e.g., notes_dir from env var), and scope-specific behaviors (e.g., comments only for Bilibili). It also explains overrides like 'explicitly passed always overrides defaults'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy but efficiently structured with a one-sentence purpose, a bulleted parameter list, and a follow-up workflow note. Each bullet covers a distinct parameter or behavior without redundancy. The front-loaded summary ensures immediate grasp of the tool's core function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (16 parameters, no annotations, async behavior), the description is remarkably complete. It covers return values, asynchronous polling, alternative tools, parameter semantics, defaults, and environment variable fallbacks. An output schema exists, but the description provides all necessary context not inferable from the structured fields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage, so the description carries full parameter documentation. Every parameter is explained with its role, allowed values (e.g., style enum list, format array examples), defaults, and dependencies (e.g., provider_id requiring list_providers/add_provider). This far exceeds the minimal schema information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a clear statement: '提交一个视频链接/本地文件,异步生成 AI Markdown 笔记' (submit a video link/local file, asynchronously generate AI Markdown notes). This specifies the verb (generate), resource (AI Markdown notes from video), and async nature, clearly distinguishing it from the sibling tool prepare_note_material.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly mentions the alternative tool: '只需素材(转写/帧/评论,不调 LLM 总结)供自行写笔记时,用 prepare_note_material' (if you only need raw materials without LLM summarization, use prepare_note_material). It also outlines the full async workflow, including using get_task_status / wait_for_note to query results, providing clear when-to-use guidance.

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

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