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local_delegate

Delegate mechanical text-to-text tasks to a local LLM to save Claude subscription quota. It constructs prompts with guardrails and returns output in the specified format.

Instructions

Tool genérica de escape: delega una tarea texto->texto a un modelo local.

Úsala cuando ninguna tool específica encaje. Arma el prompt con guardrails y devuelve texto.

Args:
    task: Instrucción de la tarea (una frase con formato de salida explícito).
    input: Contenido sobre el que operar.
    output_format: Formato exacto de salida esperado.
    model: Modelo a usar; uno de los ids configurados en el catálogo. Por defecto el mecánico.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
inputYes
modelNo
output_formatYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries full burden. It mentions basic behavior (text-to-text, guardrails, default model) but lacks details on side effects, rate limits, auth, or state changes. Adequate but minimal.

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?

Extremely concise: one sentence for purpose, one for usage, one for guardrails, then clean bullet-style Args. Every sentence earns its place, no redundancy.

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 generic text-to-text tool with an output schema (provided separately), the description covers all essential aspects: role, parameters, output type. No gaps given the tool's simplicity.

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

Parameters5/5

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

Schema description coverage is 0%, but the description's 'Args' section explains each parameter: task (format instruction), input (content to operate on), output_format (exact output format), model (catalog IDs, default mechanical). Adds significant meaning beyond schema titles.

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 clearly states the tool is a generic fallback for text-to-text delegation to a local model (verb 'delega'). It explicitly differentiates from sibling tools by saying 'Úsala cuando ninguna tool específica encaje' (use when no specific tool fits).

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?

Provides explicit guidance on when to use the tool: 'cuando ninguna tool específica encaje'. Implies it's a last resort but does not list alternative tools by name or state explicit exclusions.

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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