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Glama

valuation-api

calculate_relever_beta

Read-onlyIdempotent

Relever an unlevered (asset) beta to a target capital structure using the Hamada formula — restoring financial risk for the specific debt/equity mix of the company or deal being valued. Formula: Beta(levered) = Beta(unlevered) x (1 + (1 - tax rate) x Debt/Equity). WHEN TO USE: Use AFTER unlevering comparable betas: apply the average unlevered beta to your target company’s (or transaction’s) capital structure to obtain the beta for WACC. WHEN NOT TO USE: Do NOT relever onto an unrealistic target structure — extreme leverage produces extreme betas that may overstate risk; sanity-check the resulting cost of equity. 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 { levered_beta: number (e.g. 1.15), inputs }. PARAMETERS: unlevered_beta (required): Unlevered (asset) beta, e.g. 0.85. 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): Target debt-to-equity ratio (market values preferred), e.g. 0.6 = 0.6x. Must be >= 0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tax_rateYesCorporate tax rate as a decimal between 0 and 1, e.g. 0.25 = 25%.
debt_to_equityYesTarget debt-to-equity ratio (market values preferred), e.g. 0.6 = 0.6x. Must be >= 0.
unlevered_betaYesUnlevered (asset) beta, e.g. 0.85. 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 (readOnlyHint, idempotentHint, destructiveHint), the description discloses additional useful behavior: the calculation is deterministic with no side effects, and invalid inputs such as division by zero or non-finite values return explicit errors rather than numbers. This goes well beyond what annotations already state and prepares the agent for error handling.

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 clear headings (formula, WHEN TO USE, WHEN NOT TO USE, BEHAVIOUR, RETURNS, PARAMETERS) and is front-loaded with the key formula and purpose. It is somewhat lengthy, and the PARAMETERS section repeats information already present in the schema, which is slightly redundant. Overall it earns its length with actionable guidance, just not perfect conciseness.

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?

The description fully covers the tool's purpose, formula, usage conditions, error behavior, and return format. Since there is no output schema, the RETURNS section specifying the JSON object structure is especially valuable. All necessary information for an agent to correctly select and invoke this tool is present.

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 baseline is 3. The description's PARAMETERS section largely duplicates the schema text rather than adding new meaning. It does reinforce the constraints and gives examples, but the formula and the relationship between parameters are already evident from the description's opening. No significant semantic value is added beyond the schema.

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 precise verb and object: 'Relever an unlevered (asset) beta to a target capital structure using the Hamada formula.' It also states the exact formula and the role of the tool in restoring financial risk for a specific debt/equity mix. This clearly distinguishes it from its sibling calculator, calculate_unlever_beta, by focusing on the relevering direction.

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 contains explicit WHEN TO USE and WHEN NOT TO USE sections. It instructs the agent to apply this tool after unlevering comparable betas and warns against relevering onto unrealistic capital structures. This gives clear operational guidance and a boundary condition, leaving no ambiguity about when to invoke this tool versus an alternative.

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
Disambiguation5/5

Each tool maps to a distinct financial formula or calculation, and the when-to-use/when-not-to-use guidance cleanly separates closely related cash-flow metrics like NPV, IRR, and MOIC as well as CAPM, WACC, and DCF. There is no pair of tools that appears to perform the same operation.

Naming Consistency4/5

The overwhelming majority of tools follow a consistent calculate_<metric> snake_case pattern with descriptive names. irr_sensitivity breaks the pattern by omitting the calculate_ prefix, and mixing expanded names like cost_of_equity with abbreviations like wacc and moic is a minor deviation.

Tool Count5/5

Twelve tools is well within the ideal range for a focused financial-calculations server. Each tool covers a distinct valuation, discount-rate, or return-metric need without redundancy, so the count feels appropriately scoped.

Completeness4/5

The tool set covers the core valuation workflow: cost of equity, beta unlevering/relevering, WACC, DCF, enterprise-value multiples, and investment return metrics. It lacks a reverse equity-value calculation and an equity-side multiple like P/E, but those are workable gaps rather than severe dead ends.

Resources