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everlane_store

Fetches normalized Everlane storefront metadata as structured JSON. No URL input needed; server resolves the store source automatically.

Instructions

Get Everlane store metadata. Returns normalized storefront metadata for Everlane (https://www.everlane.com), sourced from credential-free storefront JSON. This endpoint is a brand-pinned wrapper around the generic Shopify store family: the storefront URL is fixed server-side, so no url parameter is accepted. If the vanity domain blocks /products.json, the service may fall back to a public *.myshopify.com domain discovered from the storefront page, or to the storefront's own embedded page data for storefronts that expose neither.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it delivers: it states the data is sourced from 'credential-free storefront JSON' (no auth), explains the fixed URL design, and details the fallback chain (vanity domain → public *.myshopify.com domain → embedded page data). This is genuinely rich behavioral context beyond the bare purpose statement.

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?

The core purpose is front-loaded in the first sentence, and each subsequent sentence earns its place (normalization/source, wrapper relationship, fallback behavior). The final sentence is somewhat dense and long, packing multiple fallback branches into one clause, which slightly reduces scannability.

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?

For a simple zero-parameter metadata tool with no output schema, the description is nearly complete: it covers input constraints, data source, auth expectations, and fallback behavior. The remaining gaps are the precise fields included in 'storefront metadata' and what happens when all fallback sources fail.

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

Parameters5/5

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

The tool has zero parameters, so the baseline is 4; the description exceeds it by explaining WHY no `url` parameter is accepted ('the storefront URL is fixed server-side'). This preempts the most likely agent error of attempting to pass a URL to a zero-parameter tool and clarifies the schema's emptiness.

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?

Description states a specific verb+resource: 'Get Everlane store metadata' and specifies the output ('normalized storefront metadata for Everlane'). It also distinguishes itself from the generic Shopify store family by identifying itself as a 'brand-pinned wrapper,' so an agent can tell it apart from shopify_store and other brand-pinned store tools.

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

Usage Guidelines4/5

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

The description gives clear context: it is Everlane-specific, the URL is fixed server-side, and no `url` parameter is accepted — which tells the agent this tool cannot be repurposed for other brands. However, it does not explicitly name sibling alternatives or state 'use shopify_store for other stores' / 'use everlane_product for products,' leaving the comparison mostly implicit.

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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