ml_get_itemRetrieve a Mercado Livre item's summary, attributes, and plain-text description using its item ID; the description is null if none exists.
Read-only. Return one item with its summary, attributes and plain-text description (null when the item has no description).
| Name | Required | Description | Default |
|---|---|---|---|
| item_id | Yes | Item id, for example MLB000000001. |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so 'Read-only' is partly redundant. The description still earns credit by disclosing the return shape — summary, attributes and text description, including the null-when-absent case — which matters because no output schema exists.
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?
Two compact sentences with zero filler, and the read-only constraint plus the return payload are front-loaded before any detail. Every clause carries information.
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?
With one required parameter, no output schema and fully covered annotations, the description covers the important unknowns: what fields come back and that description may be null. It omits error behavior for an unknown item_id, which keeps it short of complete.
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 coverage is 100% and the single item_id parameter already carries a format example ('MLB000000001'), so the schema does the heavy lifting. The description adds no further semantics about id format, source, or lookup failure behavior, so the baseline 3 applies.
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 ('Return one item') and enumerates the returned payload (summary, attributes, plain-text description), which clearly separates it from ml_list_items and ml_update_item. It stops short of naming a sibling explicitly, so it is clear but not maximally differentiated.
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 leading 'Read-only' hints at when the tool is appropriate, and singleton retrieval versus the ml_list_items sibling is implied by 'one item'. However, there is no explicit condition, no guidance on when to prefer ml_get_item_visits or ml_api_get, and no statement about what happens for a missing id.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.