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quick_inventory_diagnosis

Low-friction inventory health estimate for Shopify merchants. Use this when the merchant doesn't have precise inventory figures — requires only monthly revenue, SKU count, and industry segment. Inventory value and dead stock are estimated from industry benchmarks; all assumptions are returned transparently. Returns a 0–100 health score, risk flags, plain-language diagnosis, and prioritised recommended actions. Ideal for AI-assisted lead qualification and first-contact diagnostics. For a precise score using actual inventory figures, use inventory_health_score instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
segmentYesIndustry segment — used to select benchmark targets
skuCountYesNumber of active SKUs
grossMarginPctNoGross margin % (0–100). Uses industry default if not provided.
monthlyRevenueYesMonthly revenue in USD

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden. It discloses that inventory value and dead stock are 'estimated from industry benchmarks,' that assumptions are 'returned transparently,' and it enumerates the output contents (score, flags, diagnosis, actions). This gives a clear picture of a read-only diagnostic operation with no hidden side effects.

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 four sentences, front-loaded with the core purpose, then usage, outputs, and alternative. Every sentence earns its place with no redundancy or filler, making it appropriately sized and easy to scan.

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 no output schema, the description comprehensively covers return values (0–100 health score, risk flags, diagnosis, actions) and the estimation methodology. It also clarifies when to use this tool versus the alternative, making it complete for the tool's complexity.

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 reinforces which parameters are required ('requires only monthly revenue, SKU count, and industry segment') but doesn't add additional semantic detail beyond what the schema already provides for each parameter.

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 clearly states the tool's purpose: 'Low-friction inventory health estimate for Shopify merchants.' It specifies the verb 'estimate' and the resource 'inventory health,' and explicitly differentiates from the sibling tool inventory_health_score by noting 'For a precise score using actual inventory figures, use inventory_health_score instead.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance: 'Use this when the merchant doesn't have precise inventory figures.' It also names the alternative tool for precise figures and adds an ideal use case ('AI-assisted lead qualification and first-contact diagnostics'), making the usage context unambiguous.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: precise inventory scoring, estimated inventory scoring, and landed cost calculation. The quick_inventory_diagnosis description explicitly differentiates it from inventory_health_score, removing any ambiguity.

Naming Consistency4/5

All tool names use snake_case and follow a consistent descriptive noun-phrase pattern (e.g., inventory_health_score, landed_cost_calculator). While they don't use a verb_noun convention, the style is uniform and predictable.

Tool Count5/5

Three tools is well-scoped for the server's purpose of providing inventory health diagnostics and landed cost analysis. Each tool covers a distinct need without unnecessary overlap or bloat.

Completeness5/5

The surface covers the core domain: precise and estimated health scores plus a detailed cost calculator. For this analytics-focused server, there are no obvious missing operations or dead ends.

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