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valuation-api

irr_sensitivity

Read-onlyIdempotent

Compute an IRR sensitivity grid across a range of exit multiples and hold periods for a single lump-sum investment. WHEN TO USE: to stress-test how the annualised return varies with exit multiple and holding period before committing to an investment. Complements calculate_irr. WHEN NOT TO USE: when you need one precise IRR for a known exit value (use calculate_irr), or a full valuation (use calculate_dcf). BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive. NOTE ON GRID GEOMETRY: the byMultiple grid is computed at the SECOND hold period in hold_periods (default 5 years); the byHoldPeriod grid is computed at a 2.5x exit multiple. RETURNS: JSON object { byMultiple: { "2.0x": 14.9, ... } with IRR values as percentage numbers rounded to 1dp, byHoldPeriod: { "5y": 18.4, ... } }. PARAMETERS: initial_investment (number > 0), exit_multiples (optional array of numbers to test, default [1.5, 2.0, 2.5, 3.0, 3.5]), hold_periods (optional array of positive integers (years) to test, default [3, 5, 7, 10]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hold_periodsNoHold periods in whole years to test, e.g. [3, 5, 7, 10]. Defaults to [3, 5, 7, 10].
exit_multiplesNoExit multiples to test, e.g. [2.0, 2.5, 3.0, 4.0, 5.0]. Defaults to [1.5, 2.0, 2.5, 3.0, 3.5].
initial_investmentYesAmount invested up front, in currency units, e.g. 100000. Must be positive.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds valuable behavioral context: pure deterministic calculation, no side effects, no network/storage access, and the unusual grid geometry where byMultiple uses the second hold period and byHoldPeriod uses a 2.5x multiple. It also specifies rounding to 1dp and the exact return shape.

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 every section earns its place: purpose, usage, behavior, grid geometry, return format, and parameters are all labeled and front-loaded. The structure makes it easy for an agent to parse the critical routing and output details quickly.

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 details the return JSON structure with example keys and values, grid geometry, defaults, and rounding. Given the tool's moderate complexity and rich annotations, nothing essential is missing for an agent to invoke it 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 all parameters and constraints. The description repeats the defaults and adds brief examples, but it does not significantly expand on the schema's meaning. This meets the baseline for a fully documented 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 specific verb and resource: 'Compute an IRR sensitivity grid across a range of exit multiples and hold periods for a single lump-sum investment.' It clearly differentiates the tool from calculate_irr and calculate_dcf by naming them as alternatives, so an agent can select this tool without inspecting sibling schemas.

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 includes explicit 'WHEN TO USE' and 'WHEN NOT TO USE' sections, telling the agent to use this for stress-testing returns across scenarios and to use calculate_irr for a single precise IRR or calculate_dcf for full valuation. This is direct, unambiguous routing guidance.

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.

Resources