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efficiency

calculate_asset_turnover

Read-onlyIdempotent

Calculate asset turnover: net sales divided by average total assets — how efficiently a company generates revenue from its asset base. Formula: Asset Turnover = Net Sales / Average Total Assets. WHEN TO USE: Use to compare revenue productivity across companies or years; a falling ratio suggests assets are not generating sales efficiently. WHEN NOT TO USE: Do NOT compare asset turnover across industries — capital intensity differs fundamentally (software vs manufacturing). 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 { asset_turnover: number (e.g. 0.85 = 0.85x per year), inputs }. PARAMETERS: net_sales (required): Net sales / revenue for the period, e.g. 900000. Must be >= 0. begin_total_assets (required): Total assets at the START of the period, e.g. 1000000. Must be >= 0. end_total_assets (required): Total assets at the END of the period, e.g. 1100000. Must be >= 0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
net_salesYesNet sales / revenue for the period, e.g. 900000. Must be >= 0.
end_total_assetsYesTotal assets at the END of the period, e.g. 1100000. Must be >= 0.
begin_total_assetsYesTotal assets at the START of the period, e.g. 1000000. Must be >= 0.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark the tool read-only, idempotent, and non-destructive, but the description goes further by stating it is a 'pure deterministic calculation' with no side effects, no network or storage access, and identical outputs for identical inputs. It also discloses error behavior for division by zero and non-finite inputs, which is valuable 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.

Conciseness4/5

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

The description is well-structured with labeled sections and front-loads the core formula before usage and behavior details. It is slightly redundant with the schema's per-parameter descriptions, but each section serves a clear purpose and the examples improve usability.

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?

For a simple 3-parameter calculation tool with rich annotations, the description covers everything needed: formula, parameter semantics with examples, return format, invalid-input behavior, and usage guardrails. No critical guidance is missing.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value by explicitly linking the parameters through the formula and providing concrete numeric examples for each input, reinforcing constraints like non-negativity. It does not contradict the schema and clarifies how the three inputs combine.

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 ('Calculate asset turnover') and states the exact formula: net sales divided by average total assets. The 'average total assets' phrasing clearly distinguishes this from close siblings like fixed_asset_turnover and inventory_turnover.

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 has explicit WHEN TO USE and WHEN NOT TO USE sections, giving concrete use cases and a clear exclusion: do not compare asset turnover across industries due to capital intensity differences. This is exemplary guidance for selecting this tool over 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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