MercadoLibre MCP Server

by dedero1985

Overview Schema Source Related Servers Discussions Admin

Predict Category

predict_category

Predict the correct MercadoLibre category for a product title and improve listing accuracy. Get accurate category suggestions for your marketplace.

Instructions

Predict the best matching MercadoLibre category for a given product title.

Usage examples:

"Which category should I use for 'iPhone 15 Pro Max 256GB'?" → site_id="MLA"
"Predict category for 'Zapatillas Nike Running Hombre'" → site_id="MLU"

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

Input Schema

Name	Required	Description	Default
`input`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 11, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses the core behavior (predicting a category from a title) and shows site_id selection in examples. However, it doesn't disclose what the output looks like, whether it returns a confidence score, or any rate limits or auth requirements beyond what the schema implies.

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 compact and front-loaded with the core purpose, followed by two illustrative examples. Every sentence earns its place. The markdown formatting with bold 'Usage examples' is clear. Slightly more detail on output could be added, but the structure is efficient.

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?

The tool has a nested input object, an output schema, and no annotations. The description explains the primary use case and gives examples, but doesn't describe the output format or any caveats (e.g., what happens if no category matches). Given the output schema exists, the return value is partially covered, but the description could still mention confidence or fallback behavior.

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 0%, so the description must compensate. The description explains the 'input' object's purpose via the title examples and shows how site_id is used in examples. However, it doesn't explain the 'account' parameter or the defaulting behavior of site_id/account, which the schema does document. The description adds some value through examples but doesn't fully compensate for the 0% coverage.

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 ('Predict') and resource ('best matching MercadoLibre category for a given product title'). It clearly distinguishes this from sibling tools like list_categories or get_category, which retrieve category data rather than predict a category from a title. The usage examples reinforce the purpose with concrete queries.

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 provides clear usage examples showing how to phrase queries and which site_id to use. It implies this tool is for category prediction from a title, which distinguishes it from list_categories/get_category. However, it doesn't explicitly state when not to use it or name alternative tools for category lookup.

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