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JavaScript
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shopify_search_suggest

Fetch predictive search suggestions from any Shopify storefront for a query, returning matching products, collections, and queries with optional type filters and result limits.

Instructions

Get Shopify search suggestions. Returns predictive search suggestions from any Shopify storefront for a query: matching products, collections and suggested queries. Choose which with types (default all three) and cap results per type with limit (default 10, max 20). Products carry price, availability, images and options; collections carry title, handle, description and product count.

Input Schema

NameRequiredDescriptionDefault
qYesSearch query
urlYesShopify storefront URL
limitNoMaximum results per type, defaults to 10 and supports up to 20
typesNoComma-separated suggestion types. Allowed values: product, collection, query

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the return shape (product and collection payload fields) and states the storefront is arbitrary via `url`, but says nothing about permissions, rate limits, or failure behavior for invalid storefronts. Adequate but not rich.

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-loads the core purpose in the first sentence, then layers type/limit controls and return contents compactly. Three sentences, each carrying information, with no filler.

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?

With no output schema, the description compensates by enumerating what products and collections carry (price, availability, images, options; title, handle, description, product count), so an agent can anticipate the response. Only the shape of the 'suggested queries' type is left implicit.

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 the schema already documents all four parameters including the `limit` default/max and `types` allowed values. The description largely restates these defaults rather than adding new syntax or semantics, matching the baseline for full-coverage schemas.

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+resource (predictive/autocomplete search suggestions) against a clearly defined scope: any Shopify storefront, keyed by query. It distinguishes itself from browse-style siblings like shopify_products or shopify_collections by positioning the tool as a query-driven suggestion endpoint, though it doesn't name those siblings directly.

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 by 'for a query' and the description explains how to tune `types` and `limit`, but there is no explicit when-to-use guidance and no contrast against shopify_products (full product list) or shopify_collections. An agent must infer that this is for typeahead/suggestion lookup rather than general catalog browsing.

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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