Finds Vinted categories for a listing description, returning best matches first with full category paths and leaf flags so sellers can choose the right category.
Categories where listings like text (e.g. "nintendo 3ds console") live, best first, with full path and leaf flag.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations the description carries the burden and does disclose meaningful behavior: results are ordered 'best first' and each carries a full path plus a leaf flag, which tells the agent both ranking and payload shape. It does not say how many suggestions come back or whether an exact-match miss returns empty, which keeps it from a 5.
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 sentence, front-loaded with the return content (categories) and packing ordering and per-item fields into subordinate clauses. No filler.
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?
For a one-parameter lookup with no output schema and no annotations, the description covers purpose, ordering, and returned fields — enough to call it correctly. It only lacks the result-count/empty-result behavior an agent might want when chaining into a category filter.
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 0% for the single `text` parameter, so the description must compensate — and it does, via the example "nintendo 3ds console" clarifying that `text` is a listing title/query string rather than a category id or keyword list. It adds real meaning beyond the bare `string` schema, though it says nothing about length, language, or normalization.
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?
The description states the resource (categories) and its scope (categories that listings of the given text live in), so an agent can tell this maps free text to categories. It is written as a noun phrase rather than a verb, and it does not single out the nearest sibling (list_categories) for contrast, so it stops short of a 5.
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?
Usage is implied rather than stated: you supply a listing title to discover its category. There is no explicit when-to-use, no exclusion of list_categories, and no note on when this is preferable to plain `search` or `search_filters`, leaving the agent to infer the routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.