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JavaScript
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bilibili_autocomplete

Fetch Bilibili search-box suggestions for partial text, returning deduplicated results in Bilibili's order with the query and count.

Instructions

Get Bilibili search suggestions. Returns Bilibili search-box suggestions for the partial text in q, which is required (up to 100 characters). The response has the query, a count and deduplicated suggestions in Bilibili's order. A query with no matches returns an empty suggestions array; invalid input returns 400.

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": {
      "q": "<q>"
    },
    "name": "bilibili_autocomplete"
  }
}

Input Schema

NameRequiredDescriptionDefault
qYesPartial search text

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses the response shape (`query`, `count`, deduplicated `suggestions` in Bilibili's order), the empty-array behavior for no matches, and a 400 on invalid input. It does not mention auth requirements, rate limits, or whether suggestions are localized, which are the remaining gaps.

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

Conciseness5/5

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

The purpose sentence is front-loaded, followed by the parameter constraint, the return shape, and edge-case behavior in three tightly written sentences. Nothing is redundant and every clause adds information an agent would otherwise have to guess.

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

Completeness5/5

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

For a single-parameter autocomplete endpoint with no output schema, the description covers purpose, parameter constraints, response structure, and error/empty behavior. An agent has everything needed to call it correctly and interpret the result without additional documentation.

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?

Only one parameter exists and schema coverage is 100%, so the baseline is 3. The description adds value beyond the schema by stating the parameter is required and capped at 100 characters, a constraint absent from the schema text 'Partial search text'.

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?

The description gives a specific verb+resource ('Get Bilibili search suggestions') and narrows the scope to search-box autocomplete on partial text, which cleanly separates it from content siblings like bilibili_popular or bilibili_ranking. It never names an alternative tool, so the routing signal is implicit rather than explicit.

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?

Usage is only implied: the agent can infer that `q` is a partial string and that the tool is for typeahead-style lookup. There is no explicit when-to-use or when-not, and no pointer to sibling autocomplete tools (e.g. baidu_suggest, google_suggest) to disambiguate platform choice.

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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