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list_shopify_inventory

List product variant inventory levels from the connected Shopify store — SKU, quantity, and which product each variant belongs to. Optional query (Shopify search syntax) filters by product/variant. Use to check current stock levels before restocking or listing decisions.

Routing: Shopify inventory: variant stock levels (SKU/quantity) from the live store

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many variants to return (default 20, max 50)
queryNoOptional Shopify variant/product search, e.g. "sku:ABC-1" or "product_title:shampoo"
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. The verb 'List' clearly implies a read operation with no modification, and it mentions query filtering, but it does not explicitly state side-effect-free behavior, authentication requirements, or rate limits. It is 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?

The description is concise and front-loaded, with a clear one-sentence purpose followed by a practical usage note. The 'Routing' line is slightly redundant with the first sentence, but overall every sentence earns its place without unnecessary verbosity.

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?

Given no output schema, the description compensates by naming the return fields (SKU, quantity, product association). It covers purpose, filtering, and use case. It does not discuss pagination beyond the schema's limit parameter, but for a simple list tool it is adequately complete.

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 baseline is 3. The description adds a small amount of meaning by explaining that `query` uses 'Shopify search syntax' and filters by 'product/variant,' but it does not materially extend beyond what the schema already documents.

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 uses a specific verb+resource ('List product variant inventory levels') and clearly distinguishes from sibling tools like list_shopify_products (product-level) and adjust_shopify_inventory (modifies). It also names the key output fields (SKU, quantity, product), making the tool's scope immediately clear.

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 a clear use case: 'Use to check current stock levels before restocking or listing decisions.' It implies a read-only role versus adjust_shopify_inventory, but does not explicitly name alternatives or exclusions, so it stops short of 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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TDQS

A3.6/5.0
Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

Resources