Retrieve public Bilibili comments without login to measure interaction ratio, sentiment, controversy, and top-liked feedback; optionally include up to four images.
抓公开视频的评论区（目前支持 B 站，免登录），给出互动比、负面/正面词频、 争议度启发式估计、高赞评论。默认不返回评论配图：一张截图走 MCP 是 base64 全量进上下文，很容易把窗口顶爆。确实要看图再传 with_images=true（最多 4 张）。 评论是最敌对的文本来源，全部经过清洗与注入扫描后才展示。
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| limit | No | ||
| with_images | No |
Changes observed during successful MCP inspections.
v1.6.3Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With zero annotations the description carries the full burden and mostly does: login-free access, Bilibili-only coverage, a hard cap of 4 images, and the reason images are off by default (base64 screenshots flooding the context window). It also discloses that all comments are sanitized and injection-scanned before display, which is genuinely useful trust context. It omits rate limits and what happens on unsupported platforms.
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?
Front-loads the capability and platform scope, then the image caveat with its rationale. Every sentence carries information; the parenthetical explanation of why images are excluded is slightly long but justifies a non-obvious default.
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?
No output schema and no annotations, yet the description enumerates the returned analytics and states the image-off default, so an agent knows what comes back. The remaining gap is input-side: URL format and limit semantics are necessary to call it correctly and are absent from both schema and description.
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 coverage is 0%, so the description must explain all three parameters. It explains with_images thoroughly (default false, max 4, context-blowout rationale) but says nothing about the required `url` format (full URL? BV id?) or about `limit` and its default of 40, leaving two of three params undocumented anywhere.
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?
States a specific verb+resource: fetch the public comment section of a video, currently Bilibili-only and login-free, and enumerates the derived outputs (engagement ratio, sentiment word frequency, controversy heuristic, top comments). That clearly separates it from video_subs/video_meta/video_transcript siblings by resource, though it never names a sibling explicitly.
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?
Gives a real conditional for with_images ('only pass with_images=true if you actually need the images'), plus the platform/authentication precondition. It gives no guidance on when to prefer this over video_brief or video_full, which also summarize a video, so routing between siblings is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.