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bilibili-learning-mcp

bilibili-learning-mcp

by Unc1e-2ack
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get hot Bilibili comments

get_hot_comments

Fetch a Bilibili video's popular comments ranked by likes to judge video quality and audience feedback before watching or after summarizing the content.

Instructions

按点赞数排序返回视频的热门评论，用来判断视频质量和观众口碑。

何时调用：
    - 用户想知道某个视频「值不值得看」「大家评价怎么样」时。
    - 总结完视频内容后，想补充观众反馈作为参考时。
    - 不要用它替代 get_subtitles 做内容总结：评论只有观点，没有讲解内容。

参数：
    bv_id: BV 号或完整链接，例如 "BV1GJ411x7h7"。
    limit: 返回条数，范围 1-49，默认 10。

返回示例：
    《线性代数的本质》热门评论（按点赞排序，共 1234 条，返回前 2 条）：

    1. 👍 892 | 某用户
       讲得比我们老师清楚多了
    2. 👍 431 | 另一个用户
       第三集开始有点快，建议 0.75 倍速

失败时：
    返回以「获取评论失败：」开头的说明；评论区关闭时会明确告知，
    此时可以改用 get_subtitles 或 get_video_info 了解内容。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "bv_id": "<bv_id>"
    },
    "name": "get_hot_comments"
  }
}

Input Schema

NameRequiredDescriptionDefault
bv_idYes
limitNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations supplied, the description carries the disclosure burden and does well: it states results are sorted by like count, that comments are opinions only (no instructional content), and that failures return a message prefixed with 「获取评论失败：」 including a distinct closed-comment case. It does not cover rate limits, auth needs, or pagination beyond the limit parameter.

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

Conciseness4/5

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

Front-loaded with the one-line purpose, then clearly sectioned into when-to-call, parameters, return example, and failure behavior. The return example is slightly verbose for a definition, but it conveys sorting and truncation semantics that are otherwise unavailable.

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

Completeness4/5

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

Covers purpose, routing, parameters, return shape, and failure handling for a simple two-parameter read tool, including the closed-comments edge case. An output schema exists, so the example is bonus rather than a gap-filler, and the only missing element is guidance on how the 1-49 limit affects the response size.

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 description coverage is 0%, so the description must compensate and does: bv_id accepts either a BV number or a full URL with a concrete example, and limit is documented with its range (1-49) and default (10), which the schema lacks entirely.

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?

States a specific verb and resource ('按点赞数排序返回视频的热门评论') and adds the intent (judging video quality and audience reception). It is clearly distinguishable from siblings get_subtitles and get_video_info, which the description names explicitly.

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?

Provides explicit when-to-use triggers (user asks whether a video is worth watching, or wants audience feedback after a summary) and an explicit when-not-to-use with the alternative: don't use it in place of get_subtitles for content summarization. It also names fallbacks (get_subtitles, get_video_info) for the closed-comments failure case.

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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