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kisasa

Shopify MCP Server

by kisasa

get-collections

Query manual and smart Shopify collections with optional filters and field selection to control response size. Returns titles, handles, product counts, sort order, and smart collection rules.

Instructions

Query collections (manual & smart) with optional filtering. Supports field selection via 'fields' to reduce response size. Returns title, handle, products count, sort order, and rules for smart collections.

Input Schema

NameRequiredDescriptionDefault
firstNoNumber of collections to return (default 25, max 250)
queryNoSearch query to filter collections (e.g. 'title:Summer' or 'collection_type:smart')
fieldsNoIMPORTANT: Always specify this to minimize token usage and avoid flooding context with unnecessary data. Only the listed fields will be fetched from the API and returned. 'id' is always included. If you are unsure which fields are needed, ask the user before fetching all fields. Example: ["id", "title"] returns only collection GID and title. Available: id, title, handle, description, sortOrder, productsCount, templateSuffix, updatedAt, ruleSet, image, seo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations, so the description must carry the behavioral burden. It discloses that results include title, handle, products count, sort order, and smart-collection rules, and that 'fields' reduces response size — useful. But it omits pagination/cursor behavior, default page size (only in schema), and whether it's read-only (only implied by 'Query').

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?

Three compact sentences, front-loaded with purpose and scope, followed by the fields optimization and return shape. No filler; could be marginally tighter by merging the last two clauses.

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 and no annotations, the description covers the essentials: what's queried, the returned fields, and the token-saving fields option. The remaining gap is pagination and default-limit behavior, which are handled in the schema but not reinforced here.

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 coverage is 100%, so the schema already fully documents first, query, and fields. The description echoes filtering and field selection without adding syntax, format, or edge-case meaning beyond what the schema provides; 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 the specific verb and resource ('Query collections') and adds scope detail distinguishing manual from smart collections with optional filtering. It's clear what the tool does, though it doesn't explicitly contrast with the sibling get-collection-by-id.

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?

Implies usage through 'optional filtering' and the field-selection advice, but never states when to use this vs get-collection-by-id or get-products, nor any prerequisites. Usage is inferable rather than guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.