Skip to main content
Glama

apply_prompt_instruction

Apply natural language instructions to modify a prompt (add, change, remove). Preview the result, then use save_prompt to commit changes.

Instructions

Aplicar instruccion al prompt — Modifica el prompt existente segun una instruccion en lenguaje natural. ESTA ES LA ACCION CORRECTA cuando el usuario quiere añadir, cambiar o quitar algo del prompt (ej: 'add a rule about...', 'anade horario', 'add a shop-level rule', 'quita la parte de devoluciones', 'hazlo mas formal'). 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. [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?

Without annotations, description carries full burden. It discloses that the tool returns a preview without saving, automatically reads current prompt if omitted, and requires a follow-up save. Could mention error behavior or permissions, but overall transparent.

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?

Highly concise with imperative structure. Key information is front-loaded. Important directives are capitalized for emphasis. Every sentence adds value without redundancy.

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 no output schema, description adequately explains return value ('devuelve el prompt modificado como preview') and workflow. Slightly incomplete on potential errors or limitations, but sufficient for agent to use correctly.

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 already describes both parameters (100% coverage). Description adds context: for 'prompt', it clarifies optional behavior and eliminates need for prior get_prompt call; for 'instruction', provides usage examples. Adds clear value beyond schema.

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 'Modifica el prompt existente segun una instruccion en lenguaje natural' with specific verb and resource. It explicitly marks this as the correct action for adding/changing/removing prompt content and distinguishes itself from saving via 'NO guarda los cambios'.

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?

Provides explicit when-to-use context: 'ESTA ES LA ACCION CORRECTA cuando el usuario quiere añadir, cambiar o quitar algo del prompt' with concrete examples. Also clearly states that after using this, the agent must call save_prompt: 'DEBES llamar a save_prompt para guardar los cambios'.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/wazionapps/mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server