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kyuza1
by kyuza1

random_character

Randomize character skin, hair, clothing, footwear, and gear, returning item data and preview URL to review before generating a final spritesheet. Use seeds to repeat draws; fixed items always appear.

Instructions

Sorteia um personagem (pele, cabeça, cabelo, roupa, calçado e às vezes barba, chapéu, colete ou capa). Não gera a imagem: devolve {items, body_type, url} para revisar e passar a generate_character. fixed_items entram em todos (ex.: uma arma). Use seed para repetir o mesmo sorteio.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
body_typeNomale
fixed_itemsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
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 well: it discloses the exact return shape ({items, body_type, url}), clarifies that no image is generated, and explains that `fixed_items` are injected into every draw. It omits whether the operation is side-effect free or cached, but the substantive behavioral facts are covered.

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?

Four tight sentences, front-loaded with what is drawn and the key negative constraint (no image generation) before the return value and parameter notes. No filler; the parenthetical slot list is the only slightly dense part.

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?

No annotations and no output schema, so the description must supply the return contract, which it does explicitly. It covers the mutating-workflow handoff to generate_character and the two most consequential parameters; only the `body_type` input semantics remain unstated.

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 description coverage is 0%, so the description must compensate, and it explains two of three parameters meaningfully: `seed` reproduces the same draw and `fixed_items` are forced into every result (e.g., a weapon). The `body_type` input (default 'male') is only implied via the return shape, leaving a small gap.

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?

States a specific action (random draw of a character) and enumerates the slots involved (skin, head, hair, clothing, footwear, plus optional beard/hat/vest/cape). It explicitly distinguishes itself from the sibling generate_character by saying it does NOT produce the image, so an agent can route correctly without opening either schema.

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

Usage Guidelines4/5

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

The description defines the workflow clearly: this tool returns {items, body_type, url} for review, which you then pass to generate_character, and `seed` lets you reproduce a draw. That is actionable when-to-use guidance, though it never states exclusions (e.g., when to prefer preview_character or generate_batch instead).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.