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goat_product

Fetches GOAT product metadata plus live per-size/condition pricing and stock from a URL slug to guide sneaker research and buying decisions.

Instructions

Get GOAT product detail. Returns a GOAT product: identity/descriptive metadata (name, brand, SKU, colorway, designer, silhouette, taxonomy, materials, release date, retail price, editorial story, images, full size range, other products featured alongside it), plus live per-size/condition pricing and stock status (lowest price, GOAT Instant Ship price, last sold price, highest current buyer offer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesGOAT product URL slug, the path segment of a https://www.goat.com/sneakers/{slug} product page
country_codeNoISO 3166-1 alpha-2 country code used to localize per-size pricing, defaults to US

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses a lot about the response shape (static metadata plus live per-size/condition pricing and stock figures), which is genuinely useful context. However, it says nothing about the read-only nature, auth/permission requirements, rate limits, or what happens when a slug is invalid.

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?

Two compact sentences, front-loaded with the core action before enumerating returned fields. The dense parenthetical lists are somewhat heavy but each item is informative and nothing is redundant.

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?

No output schema exists, so the description usefully itemizes what the response contains (identity metadata, pricing, stock). Combined with the fully documented 2-parameter schema, an agent has what it needs to call this correctly; only usage routing guidance 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%, so both slug and country_code are already well documented in the schema. The description mentions 'live per-size/condition pricing' but does not tie that to country_code's localization role, adding no meaning beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Get) and resource (GOAT product detail), and the detailed return summary makes clear this fetches a single product by slug rather than a list/search result. It doesn't explicitly name siblings like goat_search or goat_product_recommended, so differentiation is by implication of scope rather than an explicit contrast.

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 when-to-use or when-not-to-use guidance. The required slug parameter implies the tool is for looking up a known product, but the description never states that a slug must first come from goat_search/goat_suggest, nor when to prefer this over goat_product_recommended.

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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