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SiteCheck Shop & Social

Ecommerce Zazzle

ecommerce_zazzle
Read-only

Zazzle (US) product search from the public search page: product ID, title, product type, price and current sale price in USD, discount %, promo label, all-time order count, store name and handle, image and product URL; 60 products per page plus the total number of matching products and pages. Prices are Zazzle's default-configuration prices at the time of the call. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch words, e.g. mug, tote bag, wedding invitation (required, 2-80 chars)
pageNoPage number 1 to 16 (default 1)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior5/5

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

Annotations already cover read-only and open-world, yet the description adds material context the agent cannot get elsewhere: $0.002 USDC per successful call via x402 on Base/Solana/Arc, failed calls not charged, and prices being Zazzle's default-config values at call time. The billing and pricing-caveat disclosures are exactly the kind of behavior an agent needs before invoking a paid tool.

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 verb and platform, then fields, then pagination, then pricing — a logical order. The field enumeration sentence is dense and long, but each element (fields, page size, totals, price, payment rails, no charge on failure) carries real information, so little is wasted.

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

Completeness5/5

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

For a 2-parameter paid search tool with an output schema and read-only annotations, the description covers source, result fields, page size, totals, price currency and configuration caveat, payment mechanism, and failure billing. Nothing an agent needs to decide or invoke correctly is missing.

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?

Schema description coverage is 100% and both parameters (q, page) are documented in the schema, so baseline 3 applies. The description only marginally supplements this by noting 60 products per page and the returned total page count, which gives the page parameter some meaning without adding syntax beyond the schema's '1 to 16'.

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?

Names a specific verb+resource (product search) on a named platform (Zazzle US) and enumerates exactly which fields come back (product ID, title, price, sale price, discount, order count, store, image/URL). With ~70 ecommerce siblings, the platform specificity alone lets an agent distinguish it from every other tool in the list.

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?

Usage is implied rather than stated: 'from the public search page' scopes the source to public US listings and '60 products per page' hints at paginated browsing, but there is no explicit when-to-use, when-not-to-use, or named alternative (e.g., other print-on-demand platforms). The agent must infer the use case.

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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