Search Items

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

With no annotations provided, the description carries the full behavioral burden, and it meets it well by disclosing that this is a read-only operation on public data accessible to any authenticated profile across countries. It does not mention pagination behavior or rate limits, but the presence of an output schema covers return structure. This is solid disclosure given zero annotation support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One compact purpose sentence followed by three scannable examples. No filler, no repetition of schema content, and the most important semantic (scope + public data) is front-loaded. Each of the three examples earns its place by demonstrating a distinct filter scenario (basic, price-filtered, different site).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Purpose and common usage patterns are well covered, and the output schema covers return shape. However, the description never flags that all parameters must be wrapped in a nested 'input' object — the examples show flat parameter names, which could lead an agent to pass them at top level. No guidance on combining filters or on the offset/limit pagination workflow is given beyond what the schema states.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for the counted top-level parameter, so the description must compensate — and it does, with three worked examples that show precisely how to populate query, site_id, and price_max from colloquial user requests. This adds practical meaning far beyond the schema's bare type definitions, teaching the agent to transform intent into parameter values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Search for products on MercadoLibre across a site') and immediately clarifies scope ('public data... regardless of which country'). This clearly distinguishes it from siblings like list_my_items (own items), get_item (single item), and create_item (write operation). The three usage examples reinforce the exact purpose.

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

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

The usage examples map natural-language user intents to concrete parameter values (query, site_id, price_max), which teaches an agent when and how to invoke this tool. However, it never explicitly excludes alternatives (e.g., no 'use get_item when you need a single product' or 'use list_my_items for seller's own items'), leaving sibling differentiation to inference from the purpose statement.

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