Bilibili Popular Videos: App Feed Data

Ask your AI for the first batch of popular recommendations from the Bilibili app and a readable summary of the returned information. No information needs to be filled in.

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Use Toolmu's bilibili-popular-feed-bilibili Tool (71b5e254-8a81-4c0d-9f0c-08ee4ba804b0) to retrieve one page of Bilibili App popular video recommendations. No required input needs to be collected; do not ask me for page indexes, video IDs or technical parameters. For the introductory first-batch request use queryParams.idx=1 and omit queryParams.last_param. This native idx is a page index starting from 1. Continue only when I request a further batch in ordinary language and authorize its separate charge. Prepare the next page index from the confirmed previous request and use the actual last video ID from that previous response as the string queryParams.last_param. Confirm the last item and identifier from the actual result; do not invent an ID, assume an output field path or request that I find a technical ID. If continuation cannot be confirmed, explain in ordinary language that you cannot continue from the available result and stop before calling again. Summarize only actual returned information. Do not automatically traverse pages or promise a complete popular-video collection, rank positions, fixed fields or counts, playback or downloads. Honor my current spending authorization before running the Tool. If Toolmu is not connected, follow https://toolmu.com/get-started.

Task:
Get the first batch of Bilibili app popular recommendations once and organize the information actually returned into a readable list.

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The final charge is based on confirmed usage. Unused reserved credits return to your available balance.

Bilibili

Bilibili Popular Videos: App Feed Data

Ask your AI for the first batch of popular recommendations from the Bilibili app and a readable summary of the returned information. No information needs to be filled in.

You receive

Receive the Bilibili app popular recommendations returned for this one batch. Your AI can organize the available information into a readable list.

Start with your task

Get the first batch of Bilibili app popular recommendations once and organize the information actually returned into a readable list.

What to know

One request retrieves one batch. Each further request is charged separately and can continue only when the previous result provides the information needed.

Inputs & outputs

Inspect the current inputs, availability, and maximum charge before starting.

Input schema

Input schema
Field / Path	Type	Requirement	Meaning
Optional pagination settings`queryParams`	`object`	Optional	Optional object containing idx and last_param. For the initial page, use the default idx of 1 and omit last_param.
Page index`queryParams.idx`	`integer`	Optional	Optional integer page index starting from 1, default 1. Use the requested page index when continuing with the previous page last video ID. Default `1`
Previous page last video ID`queryParams.last_param`	`string`	Optional	Optional string containing the last video ID from the previous page, for pagination. Omit it for the first request. For continuation, use the actual ID from the previous result.

View raw JSON

{
  "type": "object",
  "required": [],
  "properties": {
    "queryParams": {
      "type": "object",
      "properties": {
        "idx": {
          "type": "integer",
          "title": "Idx",
          "default": 1,
          "description": "Page index"
        },
        "last_param": {
          "type": "string",
          "title": "Last Param",
          "description": "Last video ID"
        }
      },
      "additionalProperties": true
    }
  },
  "additionalProperties": false
}

Result schema

Output fields have not been documented yet.

Additional notes

Read the TikHub Bilibili popular-feed endpoint reference.

Bilibili Popular Videos: App Feed Data

Bilibili Popular Videos: App Feed Data

You receive

Start with your task

What to know

Inputs & outputs

Input schema

Result schema

Connect Toolmu

Request the first batch of popular recommendations

Review the returned popular recommendations