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Glama

answer_question

Publishes a public answer to a Mercado Libre buyer question using the question ID and response text.

Instructions

Responde una pregunta en Mercado Libre. La respuesta es pública y visible por todos los compradores.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "question_id": "<question_id>",
      "text": "<text>"
    },
    "name": "answer_question"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesTexto de la respuesta
question_idYesID numérico de la pregunta a responder

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.0-alpha.3

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose an important trait: the reply is public and visible to all buyers. It omits other mutation-relevant facts an agent would want, such as whether the answer can be edited or deleted afterwards, permission requirements, or rate limits.

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?

Two short sentences, front-loaded with the action and followed by the one consequence that matters most (public visibility). There is no filler or redundancy.

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?

For a simple two-parameter mutation, the description covers the action and the public-visibility consequence, and the schema covers the inputs fully. However, with no annotations and no output schema, it should also say what a successful call produces and what preconditions or failure cases exist.

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%, and the schema documents both required parameters (question_id, text) with types and length limits. The description adds no meaning beyond that — no format hints, no constraint such as the question needing to be open — so the baseline applies.

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?

The description gives a specific verb and resource ("Responde una pregunta en Mercado Libre"), which is unambiguous and pairs naturally with the sibling list_questions. It does not explicitly contrast itself with that sibling, but the action/read distinction is obvious from the verb.

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

Usage Guidelines2/5

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

There is no guidance on when this should be used versus alternatives, and no prerequisites are stated — e.g., whether the question must still be unanswered, whether only one answer per question is allowed, or whether the seller must own the item. The agent must infer all of this from the parameter names.

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