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Valuation API

by io.github.johnbehar1500-ux

calculate_ev_to_revenue

Read-onlyIdempotent

Calculate the EV/Revenue (EV/Sales) multiple: enterprise value divided by revenue — a valuation multiple usable for companies with thin, negative or zero EBITDA (e.g. high-growth or pre-profit businesses). Formula: EV/Revenue = Enterprise Value / Revenue. WHEN TO USE: Use for valuing pre-profit / high-growth companies, or as a cross-check alongside EV/EBITDA for mature ones. WHEN NOT TO USE: Do NOT use revenue multiples alone — they ignore profitability entirely (a company can have a low EV/S and still destroy value); pair with margin and growth context. 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_revenue: number (e.g. 3.2 = 3.2x), inputs }. PARAMETERS: enterprise_value (required): Enterprise value in currency units, e.g. 10000000. Must be > 0. revenue (required): Revenue (net sales) over the trailing period, e.g. 3100000. Must be > 0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
revenueYesRevenue (net sales) over the trailing period, e.g. 3100000. Must be > 0.
enterprise_valueYesEnterprise value in currency units, e.g. 10000000. Must be > 0.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds concrete behavior: pure deterministic calculation, no side effects, no network or storage access, identical inputs produce identical outputs, and division-by-zero or non-finite inputs return explicit errors. This is exactly the kind of context annotations alone do not provide.

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 is front-loaded with the core purpose. There is minor redundancy — the formula appears both in the opening sentence and again as 'Formula: EV/Revenue = Enterprise Value / Revenue' — but overall every section earns its place.

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 two-parameter calculation, the description covers the formula, use cases, exclusions, behavioral guarantees, error handling, and return format. Since there is no output schema, the explicit RETURNS section is valuable and leaves nothing an agent needs to know to call the tool correctly.

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 schema already documents both parameters with examples and the 'Must be > 0' constraint. The description's PARAMETERS section mostly echoes the schema rather than adding new semantic meaning, so the baseline of 3 is appropriate.

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 the EV/Revenue (EV/Sales) multiple' — and immediately gives the formula. It also differentiates this from the sibling calculate_ev_to_ebitda by stating it is usable for companies with thin, negative, or zero EBITDA.

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?

Explicit 'WHEN TO USE' and 'WHEN NOT TO USE' sections tell the agent to use this for pre-profit/high-growth companies and as a cross-check alongside EV/EBITDA, while warning against relying on revenue multiples alone. This fully routes the agent to and away from 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

A4.6/5.0
Disambiguation4/5

Most tools map cleanly to distinct valuation concepts (CAPM, WACC, DCF, multiples, NPV/IRR/MOIC, beta adjustment), so an agent can generally select correctly. The main ambiguity is that calculate_irr already includes MOIC and an IRR sensitivity table, making irr_sensitivity and calculate_moic partially overlapping in purpose despite their clarifications.

Naming Consistency4/5

The overwhelming pattern is calculate_<metric>, with clear snake_case and a consistent prefix throughout. The one outlier is irr_sensitivity, which drops the calculate_ prefix and breaks the established verb_noun convention.

Tool Count5/5

Twelve tools is a well-scoped size for a valuation calculation API, covering cost of capital, DCF, multiples, and return metrics without bloat. Each tool represents a meaningful standalone calculation an agent would need.

Completeness4/5

The core valuation workflow is well covered: cost of equity, WACC, DCF, enterprise value, multiples, and investment return metrics are all present. The notable gap is the reverse of calculate_enterprise_value—deriving equity value from enterprise value—and there is no standalone terminal value calculator, though both are workable gaps.

Resources