MCP Mercado Livre

predict_category

Predict the best Mercado Livre category for a product title before publishing to avoid wrong-category rejections and improve visibility. Then check its required attributes.

Instructions

Predict the best Mercado Livre category for a product title. ALWAYS call this BEFORE create_item: create_item requires a category_id, and publishing in the wrong category hurts visibility and can be rejected. Pass the product title you intend to publish and pick the top prediction. After choosing a category, call get_category_attributes to discover which attributes are mandatory for that category. Returns a list of predicted categories with id, name and full path (e.g. 'Celulares e Telefones > Celulares e Smartphones').

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": {
      "title": "<title>"
    },
    "name": "predict_category"
  }
}

Input Schema

Name	Required	Description	Default
`title`	Yes	Product title to classify, e.g. 'iPhone 13 128GB Meia-noite'

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 15, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the disclosure burden and does well: it explains the return shape (list of categories with id, name, full path) and the consequence of misfiling. It omits auth/rate-limit behavior, but for a lightweight read-style prediction tool the operational context is largely covered.

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?

Front-loaded with the core action, then a workflow directive, then a follow-up tool, then the return format. Every sentence earns its place and none is redundant.

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?

No output schema exists, but the description explains the return values, and it embeds the tool in the publish workflow (before create_item, then get_category_attributes). Nothing an agent needs to call it correctly is missing.

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 coverage is 100% and the single 'title' param is already documented with an example in the schema, so the description adds only marginal meaning ('pass the title you intend to publish') plus guidance on picking the top prediction. 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?

States a specific verb (predict) and resource (Mercado Livre category) with the exact input (product title). It clearly distinguishes itself from siblings by positioning the tool as the pre-step to create_item, an agent can tell what it does without opening the schema.

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?

Explicitly says to ALWAYS call this before create_item and gives the reason (create_item needs category_id, wrong category hurts visibility/is rejected). It also names the follow-up tool get_category_attributes, providing a clear end-to-end workflow.

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