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shopify-multi-mcp

shopify-multi-mcp

by fledgeling-co
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Run one query on every store

graphql_query_all_stores
Read-only

Run one read-only Admin GraphQL query across multiple Shopify stores at once and compare the results side by side, each labeled by store, with shared variables and API version.

Instructions

Run the same read-only Admin GraphQL query on several stores at once and get the results side by side, each labelled with its store. For common comparisons (sales, unfulfilled orders, low stock, catalogue gaps) store_report is quicker and already combines the answers.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "graphql_query_all_stores"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesGraphQL query document.
storesNoStores to include. Omit for every connected store.
variablesNoGraphQL variables, applied to every store.
api_versionNoAdmin API version such as "2026-10". Omit for the latest stable version.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description consistently reinforces the read-only nature. It adds useful return-shape context ('results side by side, each labelled with its store') that annotations cannot convey, but is silent on fan-out failure semantics — e.g. what happens if one store errors or is rate-limited.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no waste: the capability and scope come first, the alternative-tool routing second. Every clause earns its place.

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 usefully sketches the return shape and covers the main routing decision for a four-parameter, nested-variable tool. The remaining gap is multi-store error/partial-result behavior, which an agent would need to know before fanning out a query.

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 all four parameters are already documented, including the connect_store prerequisite and the 'omit for every connected store' default. The description adds only the notion that the query and variables are identical across stores, which the schema also states, so it stays at the baseline.

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?

States a specific verb and resource ('Run the same read-only Admin GraphQL query') plus the distinguishing scope ('on several stores at once'), which separates it from the single-store graphql_query sibling. It also names what the output looks like ('results side by side, each labelled with its store').

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?

Explicitly routes the agent to store_report for common comparisons (sales, unfulfilled orders, low stock, catalogue gaps) with a reason ('quicker and already combines the answers'). It does not explicitly say to use graphql_query for single-store queries or mention the connect_store prerequisite, leaving some inference to the agent.

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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