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Glama

perguntar_local

Queries a local Ollama model to explain errors, draft text, or generate simple code without consuming Claude tokens, using code or general reasoning roles.

Instructions

Pergunta ao modelo local no PC do usuário (grátis, sem gastar tokens do Claude).

Bom para: explicar um erro, rascunhar texto, gerar código simples. papel: 'codigo' (padrão) ou 'cerebro' (raciocínio geral).

Input Schema

NameRequiredDescriptionDefault
papelNocodigo
perguntaYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; it does disclose the meaningful runtime fact that execution happens on the user's local machine at zero token cost. It omits what happens if the local model is unavailable, expected latency, and whether output quality differs from Claude — all relevant for an agent choosing this over a cloud call.

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?

Three short lines, front-loaded with the core action and the cost-saving rationale, then use cases, then parameter modes. No filler; it could be marginally tighter by folding the papel note into a single clause.

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?

An output schema exists, so return-value explanation is correctly omitted, and both parameters are covered at a basic level. What is missing for a delegation tool is failure/fallback behavior when the local model is down or slow, which an agent needs to decide whether to retry or escalate to a sibling.

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% and the schema defines no enum for 'papel', so the description is doing real work by explaining the two modes: 'codigo' (default) for code and 'cerebro' for general reasoning. 'pergunta' is left implicit, but its meaning is self-evident from the name.

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?

States a specific verb and resource: it asks the local model running on the user's own PC, with the explicit qualifier that it is free and consumes no Claude tokens. This differentiates it in spirit from siblings such as segunda_opiniao and pedir_ao_agente, though none of those siblings is named directly.

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?

'Bom para: explicar um erro, rascunhar texto, gerar código simples' gives concrete positive use cases, which is more than most definitions offer. However, it never says when NOT to use it or when to prefer the cloud/agent siblings (segunda_opiniao, pedir_ao_agente, resumir_texto), so routing remains inferential.

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