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Responder una pregunta

ml_responder_pregunta
Destructive

Publish a public answer to a buyer's question on MercadoLibre. Confirm before sending, as the response becomes visible to all and cannot be undone.

Instructions

Publica la respuesta a una pregunta EN MERCADOLIBRE, visible para todos. Es una escritura real e irreversible: mostrale el texto al usuario y esperá su confirmación antes de ejecutarla. El id es el que devuelve ml_preguntas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesID interno de la pregunta (columna ID de ml_preguntas).
textoYesTexto de la respuesta, tal como lo van a leer los compradores.
Behavior5/5

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

The description goes well beyond the annotations (readOnlyHint=false, destructiveHint=true) by disclosing that the write is irreversible ('escritura real e irreversible'), that the answer becomes publicly visible, and that the agent must obtain explicit user confirmation before executing. This is exactly the behavioral guidance an agent needs for a destructive external-state-changing tool, and it is consistent with the annotations.

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?

Three compact sentences, each carrying necessary weight: the action, the safety warning, and the ID provenance. The public-visibility and irreversibility warnings are front-loaded immediately after the main verb, which is the right placement for a destructive tool. No wasted words.

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?

For a two-parameter destructive write with full schema coverage, the description covers everything essential to invoking it correctly: what it does, the public/irreversible consequence, the required confirmation step, and where the ID comes from. The only omission is the return value or post-execution behavior, but no output schema exists, and for this tool an agent has enough to act safely.

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?

Schema description coverage is 100%, so the schema already fully documents both id and texto, including the ID column source and the fact that texto is exactly what buyers will read. The description reinforces the ID origin ('El id es el que devuelve ml_preguntas'), which is mildly useful, but it does not add meaning beyond what the parameter descriptions already provide. Baseline 3 is appropriate.

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 states a specific verb and resource: 'Publica la respuesta a una pregunta EN MERCADOLIBRE, visible para todos'. It clarifies the action is a public write to MercadoLibre, which clearly distinguishes it from read-only siblings like ml_preguntas and private-message tools like ml_mensajes. The irreversible-write warning further pins down its identity.

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?

The description gives explicit operational context: show the answer text to the user and await confirmation before executing ('mostrale el texto al usuario y esperá su confirmación'), and states the ID comes from ml_preguntas. This effectively tells the agent when and how to invoke it. However, it does not explicitly name alternatives or state when not to use it (e.g., private replies should go through ml_mensajes).

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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