MCP Mercado Livre

get_trends

Retrieve current top marketplace search terms for product research and demand discovery. Optional category_id narrows results to niche trends; returns keywords and URLs.

Instructions

Get the trending search terms on the marketplace (top searches buyers are making right now). Great for product research and demand discovery; pass a category_id to see niche trends within a specific category (find ids with get_site_categories or predict_category). Returns keyword + URL for each trend, trimmed to the requested limit.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_trends"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Max trends to return, 1-50 (default 20)
`category_id`	No	Restrict trends to a category id, e.g. 'MLB1055'. Omit for site-wide top searches

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 15, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose return content ('keyword + URL for each trend') and trimming behavior ('trimmed to the requested limit'). However, it says nothing about freshness/latency of 'right now', rate limits, or auth requirements for what is implicitly a read operation.

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?

A single tight paragraph, front-loaded with the core purpose before the use case and the category_id mechanics. Every clause earns its place and nothing is repeated.

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 read tool with no output schema and no annotations, the description covers purpose, use case, dependency routing, and return shape. Minor gaps remain around data recency and failure modes, but an agent has enough to invoke it correctly.

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 both parameters are documented there, including the category_id example and the omit-for-site-wide behavior. The description reframes category_id as 'niche trends within a specific category' but adds no format or constraint detail beyond the schema, so baseline 3 applies.

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 and resource ('Get the trending search terms on the marketplace') and immediately clarifies what that means in domain terms ('top searches buyers are making right now'). This is clearly distinguishable from siblings like get_item_visits or search_products, which measure different things.

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?

Gives an explicit use case ('Great for product research and demand discovery') and routes the agent for the category_id dependency by naming get_site_categories and predict_category. It doesn't state when NOT to use it or how it differs from related analytics siblings, so it falls just short of the top band.

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