digikala-mcp

Get Product Variants

get_product_variants

Read-only

Fetch product variant offer IDs, attributes, warranty, and prices by product_id to compare seller offers and identify exact variants for cart planning.

Instructions

List offer/variant IDs, attributes, warranty and prices from the variants endpoint.

Cached for 60 seconds. Preserve exact IDs; a variant ID identifies a seller offer, not just a color or size. Unknown availability and shipping remain unknown.

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": {
      "product_id": "<product_id>"
    },
    "name": "get_product_variants"
  }
}

Input Schema

Name	Required	Description	Default
`product_id`	Yes

Output Schema

Name	Required	Default
`error`	No
`coverage`	No	variants_endpoint
`variants`	No
`warnings`	No
`product_id`	Yes
`source_url`	No
`observed_at`	No
`cache_ttl_seconds`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 28, 2026

TDQS

B3.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds valuable behavioral context beyond the annotations: a 60-second cache, the warning to preserve exact IDs, the semantics of a variant ID as a seller offer, and the caveat that unknown availability and shipping remain unknown.

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

Conciseness4/5

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

Three compact sentences front-load what the tool returns, then give caveats. Every sentence carries useful information, though the structure could be slightly tighter by grouping the ID warnings.

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?

An output schema exists, so return values need not be explained, and the annotations cover safety. However, the description omits any explanation of the one required input parameter, leaving a notable gap for an agent invoking the tool.

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

Parameters2/5

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

There is one required parameter (product_id) and schema description coverage is 0%, so the schema documents only type and pattern, not meaning. The description does not mention product_id or explain its expected format, failing to compensate for the coverage gap despite the parameter's obvious name.

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 states a specific verb (List) and resource (offer/variant IDs, attributes, warranty, prices) and mentions the variants endpoint. It is clear what the tool returns, but it does not explicitly name or distinguish itself from close siblings such as get_product_variant_types or list_offers.

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 offers no explicit when-to-use guidance, no when-not-to-use conditions, and no alternatives. The caution about IDs and unknown availability implies care in using results, but not when this tool should be selected over siblings.

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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