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quick-game

quick-game

by danilo-1
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Recomendar skills

recomendar_skills

Interpret a natural-language game request to return its genre, technology, and required skills. Use it to guide MCP clients in selecting the right skills for each game creation stage.

Instructions

Interpreta um pedido em linguagem natural e indica gênero, tecnologia e as skills que devem ser lidas.

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": {
      "pedido": "<pedido>"
    },
    "name": "recomendar_skills"
  }
}

Input Schema

NameRequiredDescriptionDefault
pedidoYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It usefully discloses the shape of the result (genre, technology, skills to read), which partially substitutes for the missing output schema, and the tool reads as a non-mutating recommendation. It says nothing about determinism, whether a request can mismatch, or how the returned skills are meant to be consumed.

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?

A single tightly written sentence with the interpretive verb front-loaded and zero filler. It earns its place, though it is minimal enough that a short routing clause could have been added without bloat.

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

Completeness3/5

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

For a one-parameter tool with no output schema and no annotations, the description covers what the tool does and roughly what it returns, which is the minimum viable. It omits how the output connects to sibling tools (ler_skill, listar_skills) and any indication of output format, so it is adequate but gapped.

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?

There is a single required parameter ('pedido') with 0% schema description coverage, so the schema explains nothing. The description partially compensates by stating the input is a natural-language request, clarifying expected format, but adds no detail on length, language, or example content.

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 ('Interpreta') plus concrete outputs (gênero, tecnologia, skills a serem lidas), so the agent understands what the tool produces. It does not, however, distinguish itself from nearby siblings such as listar_skills or ler_skill, which it feeds into, so it stops short of a 5.

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 phrase 'um pedido em linguagem natural' implies the use case: free-form user requests rather than explicit skill names. But there is no explicit when-to-use, no when-not-to-use, and no named alternative (e.g., that listar_skills/ler_skill handle the follow-up lookup), leaving the agent to infer routing.

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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