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valuation-api

calculate_ev_to_ebitda

Read-onlyIdempotent

Calculate the EV/EBITDA multiple: enterprise value divided by EBITDA — the most widely used valuation multiple for comparing companies independent of capital structure, tax and depreciation policy. Formula: EV/EBITDA = Enterprise Value / EBITDA. WHEN TO USE: Use for relative valuation of cash-generative businesses against peer multiples or transaction comps; a lower multiple may indicate relative undervaluation (or justified risk). WHEN NOT TO USE: Do NOT use when EBITDA is negative or near zero, or for early-stage companies with no meaningful EBITDA — the multiple is meaningless there (use EV/Revenue). 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, non-finite inputs, or mathematically undefined combinations return an explicit error instead of a number. RETURNS: JSON object { ev_to_ebitda: number (e.g. 8.5 = 8.5x), inputs }. PARAMETERS: enterprise_value (required): Enterprise value in currency units, e.g. 10000000. Must be > 0. ebitda (required): Earnings before interest, tax, depreciation and amortisation, e.g. 1200000. Must be > 0 for a meaningful multiple.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ebitdaYesEarnings before interest, tax, depreciation and amortisation, e.g. 1200000. Must be > 0 for a meaningful multiple.
enterprise_valueYesEnterprise value in currency units, e.g. 10000000. Must be > 0.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructiveHint, and the description reinforces these with explicit statements: pure deterministic calculation, no side effects, no network/storage access, and idempotent behavior. It also discloses error behavior for division by zero and non-finite inputs, which adds transparency 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-structured with labeled sections for definition, formula, when to use, when not to use, behavior, returns, and parameters. It is longer than average but every section earns its place by providing operationally useful guidance.

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?

Despite having no output schema, the description explicitly states the return format: a JSON object containing ev_to_ebitda and inputs. It also covers formula, parameter constraints, error cases, and usage boundaries, making the tool fully usable without additional context.

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 input schema already documents both enterprise_value and ebitda with constraints and examples. The description repeats these parameter details rather than adding substantial new meaning, keeping this dimension at the baseline 3.

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 identifies the tool's function: calculating the EV/EBITDA multiple from enterprise value and EBITDA, with a formula and statement of its use as a valuation multiple. It also distinguishes itself from related siblings by explicitly naming EV/Revenue as the alternative when EBITDA is unusable.

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?

Contains explicit WHEN TO USE and WHEN NOT TO USE sections, including concrete conditions such as negative or near-zero EBITDA and early-stage companies. It even names the alternative metric (EV/Revenue), giving an agent clear decision criteria for selecting this tool over siblings.

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

Every tool targets a distinct financial metric or formula, and the WHEN TO USE / WHEN NOT TO USE guidance clearly separates closely related concepts like DSO vs. receivables turnover, current vs. quick vs. cash ratio, and DPI/RVPI/TVPI. Even the overlapping investment-return tools (NPV, IRR, MOIC, payback) are explicitly differentiated.

Naming Consistency4/5

The overwhelming majority of tools follow a clean calculate_<metric> snake_case pattern, making the set highly predictable. The single exception is irr_sensitivity, which breaks the calculate_ prefix convention but is still readable and not chaotic.

Tool Count2/5

At 52 tools, this far exceeds the 25+ threshold where a tool set becomes too heavy for its scope. Although each tool is a distinct pure calculation, the sheer breadth creates a large navigation and selection burden for agents.

Completeness4/5

The server covers an unusually broad range of valuation workflows: time value of money, DCF/WACC/cost of capital, relative valuation multiples, profitability/liquidity/efficiency ratios, and private equity fund metrics. Minor gaps exist, such as no dedicated free cash flow, net debt, or EBITDA margin calculators, but these are workable around.

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