Extracts sampled frames from a Bilibili video via the official API into a grid and pairs them with subtitle text, enabling an LLM to summarize what the video covers.
真正“看”一个 B 站视频：走官方 API 下载低清流（机房 IP 也可用）→ 抽 N 帧拼成网格 + 字幕文本。适合回答“这个视频讲了什么”。注意：只有字幕能反映台词/讲解，纯语音内容会缺失。
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | B 站链接 | |
| frames | No | 抽几帧，默认 9 |
Changes observed during successful MCP inspections.
v0.7.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden. It does disclose useful traits — official API, low-resolution stream, works from datacenter IPs, frame count 4–12, and the subtitle-only limitation for speech. However, it says nothing about latency/cost of downloading a full video, failure modes for unavailable or subtitle-less videos, or what the returned payload looks like, leaving significant gaps for a network-heavy tool.
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 purpose and workflow are front-loaded in a single arrow-chained sentence, followed by a use case and a caveat — no filler. The arrow-chain is slightly dense to parse, but every clause carries information.
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?
For a two-parameter tool with no annotations and no output schema, the description covers the essential mechanics, the use case, and the main limitation. It would be stronger with a note on runtime cost and on behavior when a video has no subtitles, but nothing critical for correct invocation is missing.
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 both parameters are already documented. The description adds a small amount of meaning by explaining that the extracted frames are tiled into a grid, but it does not clarify tradeoffs of choosing more vs fewer frames beyond the schema's 4–12 bounds.
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 states a specific verb and resource — actually 'watching' a Bilibili video by downloading the official low-bitrate stream and extracting N frames into a grid plus subtitle text. It also names the concrete question it answers ('what is this video about'), which separates it from bili_video_info (metadata) and media_frames (generic extraction).
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 gives a clear use condition ('适合回答"这个视频讲了什么"') and a caveat that only subtitles carry spoken/dialogue content, which implicitly steers speech-only content elsewhere. It stops short of explicitly naming the sibling tools or stating exclusions, so it falls just short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.