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apply_prompt_instruction

Modify WhatsApp Business prompts by applying text instructions to preview changes before saving them permanently.

Instructions

Aplicar instruccion al prompt — Genera una version modificada del prompt segun una instruccion. NO guarda los cambios - solo devuelve el prompt modificado como preview. Despues de obtener el resultado, DEBES llamar a save_prompt para guardar los cambios. Ejemplos: 'Anade horario de atencion de 9 a 18h', 'Quita la parte de devoluciones', 'Hazlo mas formal'. [query]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptNoEl prompt a modificar. Si no se proporciona, el endpoint lee automaticamente el prompt actual de la tienda. No es necesario llamar a get_prompt primero.
instructionYesLa instruccion de que modificar en el prompt
Behavior4/5

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

With no annotations provided, the description carries full burden and discloses key behavioral traits: it operates as a preview-only operation ('solo devuelve el prompt modificado como preview') and does not persist changes. However, it does not describe the return format structure, error behavior for invalid instructions, or any rate limiting considerations.

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?

The description is front-loaded with the action, immediately followed by critical workflow constraints (no save → must call save_prompt), then examples. The '[query]' tag at the end appears to be an artifact or metadata that doesn't add value, slightly detracting from an otherwise tight structure. Otherwise, every sentence earns its place.

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?

Given the tool has 2 well-documented parameters and no output schema, the description adequately covers the workflow by stating it returns the modified prompt as a preview. However, it could improve by briefly describing the return structure (e.g., whether it's a string or object) or mentioning error scenarios when the instruction cannot be applied.

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?

Despite 100% schema coverage (baseline 3), the description adds valuable semantics: it provides concrete examples for the instruction parameter ('Anade horario de atencion', 'Quita la parte de devoluciones'), and clarifies the auto-read behavior for the prompt parameter that isn't fully captured by the schema description alone.

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 'Genera una version modificada del prompt segun una instruccion' (generates a modified version of the prompt according to an instruction), providing a specific verb and resource. It effectively distinguishes itself from siblings by explicitly stating it does NOT save changes, contrasting with save_prompt, and noting it auto-reads the current prompt unlike get_prompt.

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

Usage Guidelines5/5

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

Excellent guidance provided: explicitly states 'NO guarda los cambios' (does not save changes) and mandates 'DEBES llamar a save_prompt para guardar los cambios' (you MUST call save_prompt to save changes). It also clarifies when prompt parameter can be omitted ('Si no se proporciona, el endpoint lee automaticamente'), preventing unnecessary get_prompt calls.

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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