Crawlora MCP

zappos_product

Read-only

One Zappos product's full detail: name, brand, description, category, breadcrumbs, pricing, images, aggregate rating with a 1-5 star breakdown, up to two featured customer reviews with real author/date/body/rating, reviewer-submitted fit feedback for size/width/arch, and every sibling color variant with its own price. product_id is taken from a search result's product_id field or a product page's URL. color_id is optional and selects a specific color variant; an omitted or invalid color_id still resolves the base product using a real color variant rather than failing.

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>",
      "color_id": "<color_id>"
    },
    "name": "zappos_product"
  }
}

Input Schema

Name	Required	Description	Default
`color_id`	Yes	Zappos color id selecting a specific color variant
`product_id`	Yes

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 7, 2026
- addedInput schema / properties / color_id / description
```
Added value: +"Zappos color id selecting a specific color variant"
```
First observedOct 5, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description adds genuine behavioral detail beyond that: the exact returned facets and, critically, the fallback rule that an omitted or invalid color_id still resolves a real color variant rather than failing. That failure-mode disclosure is valuable. It stops short of describing rate limits or auth, but the bar is lower given annotations.

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?

Front-loaded with the resource and its returned content, then moves to parameter sourcing. It is dense but every clause carries information (return facets, ID sourcing, fallback behavior). Slightly long, but no filler sentences.

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 structure needn't be explained, yet the description still summarizes it helpfully. Combined with the ID-sourcing and color_id fallback guidance, an agent has enough to call this correctly. Only minor gaps around error conditions or auth remain.

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 50% (only color_id has a description). The description compensates: it explains that color_id is optional and defines the behavior when it is omitted/invalid, and it clarifies how to source product_id from a search result or URL. This adds real semantic value the schema lacks, especially for the undocumented product_id.

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 relationship ('One Zappos product's full detail') and enumerates what that detail contains (name, brand, pricing, ratings, reviews, fit feedback, color variants). This clearly distinguishes it from siblings like zappos_search or zappos_brand, which return lists or brand data.

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

Usage Guidelines3/5

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

The description tells the agent where product_id comes from ('a search result's product_id field or a product page's URL'), which is useful routing context toward zappos_search. However, it never explicitly names the alternative tool or states when NOT to use this one, leaving the search-vs-detail decision implied.

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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