Ozon MCP

get_description

Retrieve a product's description and embedded images from Ozon using its SKU or URL. Get full product card details for analysis or display.

Instructions

Product description text plus the images embedded in it.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "sku_or_url": "<sku_or_url>"
    },
    "name": "get_description"
  }
}

Input Schema

Name	Required	Description	Default
`sku_or_url`	Yes	A product SKU ("3077454533") or a full ozon.ru product URL. Variants have their own SKUs.

Output Schema

Name	Required	Description	Default
`sku`	Yes
`images`	No
`description`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.1Sep 2, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the output content (description text and images) but does not explicitly mention side effects, read-only status, authentication needs, or any limitations. The 'get' prefix implies read-only behavior, but the description itself does not confirm it.

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 extremely concise, containing a single short phrase with no redundant words. It is appropriately sized for a simple retrieval tool and immediately communicates the core purpose.

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

Completeness3/5

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

This is a simple tool with one parameter and an output schema, so the short description is partly acceptable. However, it lacks any context on when to prefer this tool over related product-data tools, and it does not mention that it accepts both SKU and URL (though the schema covers that). Overall, it is adequate but has clear gaps in usage context.

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?

The schema fully describes the single parameter sku_or_url with 100% coverage, including examples and clarification that variants have own SKUs. The description adds no additional parameter semantics, so the baseline score of 3 applies.

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

Purpose4/5

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

The description, "Product description text plus the images embedded in it," clearly identifies what the tool returns: the product description and its embedded images. It is distinct from siblings like product_details or get_reviews by focusing specifically on the description content, but it lacks an explicit verb such as 'retrieves' or 'returns', so it is more of a noun phrase than a full command.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like product_details or search. There is no mention of exclusion conditions, prerequisites, or the specific scenario where this tool is the correct choice, leaving the agent to infer usage solely from the name.

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