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Glama

Valuation API

by io.github.johnbehar1500-ux

calculate_unlever_beta

Read-onlyIdempotent

Unlever a (levered) equity beta to its asset beta using the Hamada formula — removing the financial-risk effect of debt so betas of companies with different capital structures can be compared. Formula: Beta(unlevered) = Beta(levered) / (1 + (1 - tax rate) x Debt/Equity). WHEN TO USE: Use when valuing a private company or a deal with a different capital structure than the public comparable — unlever the comps’ betas, average them, then relever at your target structure. WHEN NOT TO USE: Do NOT unlever with an inconsistent tax rate or debt/equity ratio — the result is only as clean as its inputs; for companies with significant non-debt liabilities consider a more advanced formula. 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 { unlevered_beta: number (e.g. 0.85), inputs }. PARAMETERS: levered_beta (required): The observed (levered) equity beta of the comparable company, e.g. 1.2. Must be > 0. tax_rate (required): Corporate tax rate as a decimal between 0 and 1, e.g. 0.25 = 25%. debt_to_equity (required): Debt-to-equity ratio of the company whose beta is being unlevered (market values preferred), e.g. 0.5 = 0.5x. Must be >= 0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tax_rateYesCorporate tax rate as a decimal between 0 and 1, e.g. 0.25 = 25%.
levered_betaYesThe observed (levered) equity beta of the comparable company, e.g. 1.2. Must be > 0.
debt_to_equityYesDebt-to-equity ratio of the company whose beta is being unlevered (market values preferred), e.g. 0.5 = 0.5x. Must be >= 0.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond the annotations by stating the calculation is 'pure deterministic,' with 'no side effects, no network or storage access,' idempotent, and non-destructive. It also discloses error behavior for division by zero, non-finite inputs, and undefined combinations, which is highly useful for an agent deciding how to invoke and handle results.

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 long but well-structured with labeled sections: Formula, WHEN TO USE, WHEN NOT TO USE, BEHAVIOUR, RETURNS, and PARAMETERS. Each section earns its place, and the core purpose and formula are front-loaded before usage and behavioral caveats.

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 deterministic calculation tool with no output schema, the description covers all necessary context: formula, parameter constraints, error semantics, return shape, and use cases. An agent has everything needed to select and correctly invoke the tool.

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 strong, but the description adds the Hamada formula tying the parameters together and clarifications like market values preferred for debt_to_equity. This provides relational meaning beyond the individual parameter schemas.

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 states a specific action and resource: 'Unlever a (levered) equity beta to its asset beta using the Hamada formula.' It also explains the purpose of the calculation—removing financial-risk effect so betas with different capital structures can be compared—and gives the exact formula, making the tool's role unambiguous and distinguishable from siblings like calculate_relever_beta.

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

Usage Guidelines4/5

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

The description provides explicit WHEN TO USE and WHEN NOT TO USE guidance, including the valuation context and input-quality caveats. It mentions relevering at a target structure, which implicitly points to the alternative workflow, but it does not explicitly name calculate_relever_beta as the sibling tool to use for that step.

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.

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