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Crawlora MCP

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by Crawlora-org
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JavaScript
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bilibili_popular

Retrieve current popular Bilibili videos in the platform's order, with cursor pagination for batch-by-batch access and next cursor values.

Instructions

Get current popular Bilibili videos. Returns Bilibili's current popular videos in Bilibili's own order. Start with cursor 0 (the default; max 10000000) and pass back next_cursor for the next batch. Each video has bvid, aid, title, url, cover, duration, uploader, views_and_age and an optional reason. At the end of the feed no_more is true and videos is empty.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "bilibili_popular"
  }
}

Input Schema

NameRequiredDescriptionDefault
cursorNoUpstream cursor from the previous response; defaults to 0

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does so well: it discloses the ordering (Bilibili's own order), the pagination contract via next_cursor, and the explicit termination signal (no_more true with empty videos). It does not mention rate limits or auth, but the read-only nature and feed mechanics are otherwise clearly conveyed.

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 purpose, then return fields, then pagination and termination. It is fairly dense but each sentence adds usable information, and the field enumeration is justified since no output schema exists.

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?

For a single-param, no-annotation tool with no output schema, the description covers purpose, pagination flow, termination, and the returned fields. It is complete enough to invoke correctly; the only mild gap is not defining what 'popular' means (region/timeframe).

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

Parameters4/5

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

Schema coverage is 100% and the cursor param is documented there, but the description adds real meaning beyond it: the max value (10000000), the default, and the fact that cursor is fed from the previous response's next_cursor. That extends the schema's sparse 'defaults to 0' note.

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

Purpose4/5

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

Clear verb + resource: 'Get current popular Bilibili videos' with the specific scope of Bilibili's own ordering. It does not explicitly distinguish itself from siblings like bilibili_ranking, bilibili_weekly, or bilibili_must_watch, so an agent must infer the difference, but the core purpose is unambiguous.

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

Usage Guidelines3/5

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

The description gives solid pagination guidance ('start with cursor 0... pass back next_cursor') which tells the agent how to drive the tool. However, it offers no when-to-use/when-not guidance relative to the many other Bilibili feed siblings, leaving selection to inference.

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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