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by net.crawlora
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dramabox_suggest

Read-only

Get DramaBox typeahead suggestions for a keyword from the app API: book matches and actor/actress matches with their ids, names, cover or avatar and series count. Use a book id with dramabox_* list endpoints as needed, or an actor id with dramabox_performer. Only public catalog metadata is returned.

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": {
      "keyword": "<keyword>"
    },
    "name": "dramabox_suggest"
  }
}

Input Schema

NameRequiredDescriptionDefault
keywordYesPartial search text, trimmed and truncated to 80 characters

Output Schema

NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, and the description adds useful behavioral context: it is an app API lookup returning only public catalog metadata, with book and actor matches including ids, names, covers/avatars, and series counts. It does not contradict the annotations and adds meaningful result/privacy context.

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 description is three tight sentences, front-loads the core purpose, and every sentence adds useful information about result types, id routing, or return scope. There is no wasted or repetitive text.

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?

With an output schema present, the description does not need to explain return payload structure, but it still helpfully names the match fields and downstream id usage. It is largely complete for invocation, though it does not mention suggestion limits or any keyword constraints beyond what the schema already covers.

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

Parameters3/5

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

The single parameter is fully documented in the schema with partial-search semantics and an 80-character truncation rule. The description adds little beyond saying the input is a keyword, so this meets the baseline for high schema coverage rather than adding substantial new parameter meaning.

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?

The description uses a specific verb and resource: 'Get DramaBox typeahead suggestions for a keyword'. It clearly scopes the result to book and actor/actress matches, including the key fields returned, which distinguishes it from a full search endpoint even though it does not name a sibling search tool.

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 implies the appropriate usage through 'typeahead suggestions' and explains how to use the returned ids with downstream tools. However, it does not explicitly say when to choose this over dramabox_search or other search/list endpoints, so usage guidance is present but inferential.

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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