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Audit store listing photos

audit_store
Read-only

Audit a Shopify store's listing photos (no account needed). Scans the public product sitemap for stale photos (≥180 days), season mismatches (e.g. winter items with summer-shot photos), and upcoming retail-calendar windows. Returns work orders with pre-filled create_tryon_job fixes and estimated credit costs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
store_urlYesStore domain or URL, e.g. examplestore.com

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoMachine-readable error code (only present on failures)
storeNoNormalized store host that was audited
messageNoHuman-readable explanation of the failure
summaryNoIssue counts: stale_photos, season_mismatches, calendar_opportunities, total_work_orders, estimated_credits_to_fix_all
next_stepsNoSuggested follow-up actions
work_ordersNoPer-listing findings with a pre-filled create_tryon_job fix block
scanned_listingsNoNumber of products found in the sitemap
seasonal_contextNoRetail shoot windows currently open or urgent
work_orders_truncatedNoTrue when more work orders exist than shown

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "error": {
+      "description": "Machine-readable error code (only present on failures)",
+      "type": "string"
+    },
+    "message": {
+      "description": "Human-readable explanation of the failure",
+      "type": "string"
+    },
+    "next_steps": {
+      "description": "Suggested follow-up actions",
+      "type": "string"
+    },
+    "scanned_listings": {
+      "description": "Number of products found in the sitemap",
+      "type": "integer"
+    },
+    "seasonal_context": {
+      "description": "Retail shoot windows currently open or urgent",
+      "items": {
+        "additionalProperties": true,
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "store": {
+      "description": "Normalized store host that was audited",
+      "type": "string"
+    },
+    "summary": {
+      "additionalProperties": true,
+      "description": "Issue counts: stale_photos, season_mismatches, calendar_opportunities, total_work_orders, estimated_credits_to_fix_all",
+      "type": "object"
+    },
+    "work_orders": {
+      "description": "Per-listing findings with a pre-filled create_tryon_job fix block",
+      "items": {
+        "additionalProperties": true,
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "work_orders_truncated": {
+      "description": "True when more work orders exist than shown",
+      "type": "boolean"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark this as readOnly and openWorld, and the description adds rich behavioral context: it scans a public sitemap, checks specific criteria (≥180 days, season mismatches, retail-calendar windows), and returns work orders with estimated credit costs. This goes well beyond the annotations and provides clear expectations about what the tool does and produces. No contradiction exists.

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?

The description is exactly two sentences: the first states the core purpose and a key constraint (no account needed), the second details the scan criteria and return value. Every clause adds value, and the most important information is front-loaded. There is no wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's single required parameter and the presence of an output schema, the description is fully complete. It explains what the tool does, how it works (sitemap scan), what it detects, and what it returns (work orders with pre-filled fixes and credit costs). It also orients the user in the broader toolset by linking to create_tryon_job.

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?

The input schema has 100% coverage with a well-described 'store_url' parameter (domain/URL with example). The description does not add additional parameter semantics, so the baseline score of 3 is appropriate—the schema fully handles parameter documentation.

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?

The description opens with a specific verb and resource ('Audit a Shopify store's listing photos'), clearly distinguishing it from sibling create/get tools. It further elaborates with concrete audit dimensions (stale photos, season mismatches, retail-calendar windows), leaving no ambiguity about the tool's function.

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 states 'no account needed' and 'Scans the public product sitemap,' implying this is for public store audits without authentication. It also references 'pre-filled create_tryon_job fixes,' showing a clear workflow connection to that sibling tool. However, it stops short of explicitly saying when not to use this tool or naming alternatives, so it earns a 4 rather than a 5.

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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