digikala-mcp

Get Product Price History

get_product_price_history

Read-only

Retrieve a product's past price chart in rials with dates, seller, warranty, availability, and missing-price gaps. Check historical price changes without forecasts or interpolation.

Instructions

Read store price-chart series in rials with original calendar dates; cached 60 seconds.

Preserve missing prices, seller/warranty labels and availability. Exact variant identity is unknown; series may change sellers and exclude shipping. No interpolation or forecast.

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_price_history"
  }
}

Input Schema

Name	Required	Description	Default
`product_id`	Yes

Output Schema

Name	Required	Description	Default
`error`	No
`series`	No
`warnings`	No
`product_id`	Yes
`source_url`	No
`observed_at`	No
`selection_title`	No
`cache_ttl_seconds`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 28, 2026

TDQS

A3.9/5.0

Behavior5/5

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

Goes well beyond the readOnlyHint/openWorldHint annotations by disclosing that results are cached 60 seconds, that missing prices/seller/warranty labels/availability are preserved, that variant identity is unknown, that sellers may change, shipping is excluded, and no interpolation or forecast is performed. This is rich, decision-relevant behavioral context.

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?

Front-loaded with the core action, then dense behavioral caveats with no wasted filler. Every clause carries information an agent would need.

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?

An output schema exists, so return-value structure is covered, and the description still adds return-shape context (preserved missing prices, labels, availability). The main gap is the absence of when-to-use guidance relative to sibling series/comparison tools.

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?

The single product_id parameter has 0% schema description coverage, and the description does not compensate by explaining what the id represents or its format/constraints. The note that 'exact variant identity is unknown' concerns the returned series, not the parameter itself.

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?

States a specific verb+resource: 'Read store price-chart series in rials with original calendar dates'. This clearly distinguishes it from siblings like get_product, get_trend_snapshot, and compare_products by scoping it to a historical series with native currency/dates.

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?

No explicit when-to-use guidance and no named alternatives. The description explains behavioral constraints of the returned series but never tells the agent when to reach for this over get_trend_snapshot or compare_products, leaving selection to inference.

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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