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purchases

Search your Ozon purchase history to find past purchases by product name, and retrieve product links to reorder easily. Avoid paging through all orders.

Instructions

Everything ever bought, as product tiles (sku/title/price/url) — the answer to "have I bought this before" and "buy that thing again". With query Ozon searches its own purchase history server-side, which is much cheaper than paging through all of it. Orders, statuses and totals are not here: that is list_orders().

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoOrder of the purchase history. Ignored when a query is given (Ozon ranks the search).newest
limitNoHow many entries to return at most.
queryNoText to search the purchase history for; omit to list all of it.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.2.1

TDQS

A4.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the search behavior (server-side query) and what is excluded (orders, statuses, totals), but does not mention whether the operation is read-only, potential permissions, rate limits, or error behavior. The description gives some useful behavioral context but has clear gaps.

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?

The description is three sentences, each with a distinct purpose: define the tool, explain query behavior, and name the sibling alternative. There is no fluff or repetition, and the most important information is front-loaded.

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?

Given the output schema exists, return values need no explanation. The description covers purpose, usage, behavior, and alternatives. It could add a note on whether the history is personal or requires authentication, but for a simple list tool the current level is sufficient.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds extra meaning for the query parameter by explaining that Ozon searches server-side and that this is cheaper than paging, which is not in the schema description. No extra context for sort or limit is needed since the schema already documents them.

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 ('bought'), resource ('everything ever bought'), and presentation format ('product tiles (sku/title/price/url)'). It also names the use cases ('have I bought this before', 'buy that thing again'), which clearly distinguishes it from siblings like list_orders.

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

Usage Guidelines5/5

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

The description explicitly says that orders, statuses, and totals are not here and points to list_orders() as the alternative. It also explains when to use the query parameter (server-side search is cheaper than paging), giving clear usage context and exclusions.

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

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