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efficiency

calculate_payables_turnover

Read-onlyIdempotent

Calculate payables turnover: purchases (or COGS) divided by average accounts payable — how many times a company pays its suppliers in a period. Formula: Payables Turnover = COGS or Purchases / Average Accounts Payable. WHEN TO USE: Use to assess supplier payment speed and working-capital management; lower turnover means the company stretches supplier credit longer. WHEN NOT TO USE: Do NOT interpret low payables turnover as inefficiency without context — it can be a deliberate financing strategy. BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive; identical inputs always produce identical outputs. Division by zero or non-finite inputs returns an explicit error instead of a number. RETURNS: JSON object { payables_turnover: number (e.g. 7.5 = 7.5x per year), inputs }. PARAMETERS: cogs_or_purchases (required): Cost of goods sold or total purchases for the period, e.g. 600000. Must be >= 0. begin_payables (required): Accounts payable at period start, e.g. 70000. Must be >= 0. end_payables (required): Accounts payable at period end, e.g. 90000. Must be >= 0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_payablesYesAccounts payable at period end, e.g. 90000. Must be >= 0.
begin_payablesYesAccounts payable at period start, e.g. 70000. Must be >= 0.
cogs_or_purchasesYesCost of goods sold or total purchases for the period, e.g. 600000. Must be >= 0.

TDQS

A4.6/5.0
Behavior4/5

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

The description explicitly discloses that the calculation is pure, deterministic, idempotent, non-destructive, and has no side effects or network/storage access—which complements the annotations (which already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false). It also adds edge-case behavior (division by zero returns an explicit error), which is useful beyond the annotations.

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 well-organized with clear sections (formula, when to use, when not to use, behavior, returns, parameters), front-loading the key information. Every sentence serves a purpose and there is no redundancy.

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?

The description covers the formula, usage guidance, behavioral traits, edge cases, return format, and parameter details—comprehensive for a pure calculation tool. There is no output schema, but the description spells out the return shape, so an agent has enough to invoke it correctly. Minor gap: it doesn't specify precision/rounding for the output, but the example suffices.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% description coverage for all three parameters, so the baseline is 3. The description adds value by explaining the role of cogs_or_purchases and begin/end payables in the formula and providing realistic examples, which helps an agent understand what values to supply.

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 formula and purpose—measuring how many times a company pays its suppliers in a period. This distinguishes it from sibling tools like calculate_inventory_turnover and calculate_days_payables_outstanding, which are named as separate metrics.

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?

The description includes explicit WHEN TO USE and WHEN NOT TO USE sections, explaining when to apply the metric and warning against misinterpreting low turnover as inefficiency without context. This provides clear guidance for an agent deciding whether to invoke this tool versus alternatives.

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

A4.4/5.0
Disambiguation4/5

Each tool calculates a distinct financial efficiency ratio, but some boundaries are close: the turnover/day pairs (e.g., inventory_turnover vs. days_inventory_outstanding) cover conceptually related metrics, and asset_turnover vs. fixed_asset_turnover could initially be confused. The detailed WHEN TO USE guidance does help disambiguate, so misselection risk is low but not zero.

Naming Consistency5/5

All tool names follow the exact same calculate_<metric> pattern, making the set highly predictable and easy to scan. There are no mixed conventions, position variations, or inconsistent verb forms.

Tool Count5/5

Nine tools is a well-scoped size for a financial efficiency calculation server. Each tool corresponds to a standard, distinct efficiency ratio, and none feel redundant or unnecessary.

Completeness4/5

The set covers the core efficiency ratio family comprehensively: asset, fixed asset, inventory, receivable, and payable turnover, plus their days-based counterparts and the cash conversion cycle. Minor gaps remain, such as no direct working-capital-turnover or operating-cycle calculator, but agents can assemble those from the existing outputs.

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