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Channel3 Shopping

Get Price History

get_price_history
Read-onlyIdempotent

Fetch 30-day price history for a product, for the storefront UI.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
device_idNo
thread_idNo
product_idYesCanonical product ID.
selected_optionsNoVariant configuration as `{ optionName: label }` for re-resolving offers.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
historyYes
statisticsYes
canonical_product_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / device_id
      Added value: +{
      +  "type": "string"
      +}
  2. Changed3 schema fields changed
    • addedInput schema / properties / selected_options
      Added value: +{
      +  "additionalProperties": {
      +    "type": "string"
      +  },
      +  "description": "Variant configuration as `{ optionName: label }` for re-resolving offers.",
      +  "propertyNames": {
      +    "type": "string"
      +  },
      +  "type": "object"
      +}
    • addedOutput schema / properties / canonical_product_id
      Added value: +{
      +  "type": "string"
      +}
    • changedOutput schema / required
      Previous value: -[
      -  "history",
      -  "statistics"
      -]New value: +[
      +  "canonical_product_id",
      +  "history",
      +  "statistics"
      +]
  3. Changed1 schema field changed
    • addedInput schema / properties / thread_id
      Added value: +{
      +  "type": "string"
      +}
  4. First observed

TDQS

B3.4/5.0
Behavior3/5

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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds the 30-day window and storefront context, which is useful but limited; it does not explain behavior around variant resolution or filtered results beyond that. This is a modest improvement over the annotations.

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 one effective, front-loaded sentence with no filler. It states the core functionality and intended context without redundant detail, making it easy to scan and parse.

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?

For a read-only lookup tool backed by an output schema and active annotations, the description is mostly sufficient, but it lacks any signposting for how this tool compares to siblings and leaves some parameter behavior muddy. The core call is clear, but the surrounding decision of when to use it is omitted.

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?

Schema description coverage is only 50%, and the description does not explain any of the parameters directly. It references 'product' which maps to product_id, but leaves device_id, thread_id, and selected_options completely unexplained, so an agent cannot infer their purpose beyond the schema's partial doc.

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 names the specific verb—Fetch—and the resource: a 30-day price history for a product, with a context qualifier ('storefront UI'). This clearly differentiates the tool from siblings like search_products or get_details, which serve other purposes. The operation is unambiguous.

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?

There is no explicit guidance about when to use this tool versus the sibling product tools, and no mention of prerequisites or when not to use it. The phrase 'for the storefront UI' hints at a context but does not direct the agent toward this tool over alternatives.

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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