startup-valuation

SaaS Metrics & Valuation

valuation_saas

Read-onlyIdempotent

Compute SaaS unit economics and valuation metrics such as LTV, CAC, MRR, ARR, NRR, magic number, Rule of 40, and revenue multiples, with method selecting the formula.

Instructions

SaaS unit economics and valuation: LTV, CAC, MRR, ARR, net revenue retention, magic number, Rule of 40, CAC payback, and ARR revenue-multiple valuation. Method selects the metric. Use for subscription software; for marketplace GMV metrics use valuation_marketplace and for payments/lending use valuation_fintech. Parameters apply per method: ltv needs arpu + gross_margin + churn_rate; cac needs sales_marketing_expense + new_customers; arr needs subscription_values; nrr needs starting_revenue + ending_revenue; revenue_multiple needs arr + revenue_multiple. Not for company-level pre-revenue value — for that use valuation_core. Only method is required; all other parameters are method-dependent, so supply those the selected method names and omit the rest (defaults apply where defined). Rate and decimal inputs are fractions (0.10 = 10%); probability and weight lists are in [0,1] and sum to 1. Returns value, method, inputs, assumptions, chapter, formula_number and calculation steps; pure arithmetic — no I/O and no external calls — rounded to 2 decimals, with no auth or rate limits. An unknown method, or a missing method-required parameter, returns an error instead of a value.

Input Schema

TableJSON Schema

Name	Required	Description
`arr`	No	Annual recurring revenue, currency units.
`cac`	No	Customer acquisition cost per customer, currency units.
`arpu`	No	Average revenue per user per month, currency units.
`method`	Yes	Formula to apply. Options: ltv = LTV = ARPU × gross margin / churn.; cac = CAC = S&M expense / new customers.; mrr = MRR = ARR / 12 (reverse of ARR).; arr = ARR = Σ monthly subscriptions × 12.; nrr = NRR = (start + expansion) / start, net of churn.; magic_number = Magic Number = net new ARR / prior-quarter S&M.; rule_of_40 = Score = growth rate + profit margin.; cac_payback = Months to recover CAC from gross profit.; revenue_multiple = Valuation = ARR × multiple.
`arr_value`	No	Annual recurring revenue, currency units.
`churn_rate`	No	Periodic churn rate as a decimal (0.02 = 2% per month).
`growth_rate`	No	Revenue growth rate as a decimal (0.40 = 40%).
`net_new_arr`	No	Net new ARR added in the period, currency units.
`gross_margin`	No	Gross margin as a decimal (0.80 = 80%).
`new_customers`	No	Number of customers acquired in the period.
`profit_margin`	No	Profit margin as a decimal (0.15 = 15%).
`ending_revenue`	No	Revenue from the same cohort at period end, currency units.
`mrr_per_customer`	No	Monthly recurring revenue per customer, currency units.
`revenue_multiple`	No	SaaS revenue multiple (e.g. 8 for 8x ARR).
`sm_expense_prior`	No	Sales & marketing expense in the prior period, currency units.
`starting_revenue`	No	Revenue from the cohort at period start, currency units.
`expansion_revenue`	No	Expansion revenue from the cohort in the period.
`subscription_values`	No	Monthly subscription revenue per customer (summed x12 for ARR).
`sales_marketing_expense`	No	Sales & marketing spend for the period, currency units.

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No	Error message when the call fails.
`steps`	No	Intermediate steps for traceability.
`value`	Yes	Computed valuation or metric.
`inputs`	No	Echo of the normalised inputs used.
`method`	No	Formula / method name that produced the result.
`chapter`	No	Source textbook chapter.
`assumptions`	No	Modelling assumptions applied.
`formula_number`	No	Source textbook formula number (e.g. '3.1').
`defaults_applied`	No	Optional parameters that were not supplied, so their documented defaults were used.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv2.1.1Oct 2, 2026

addedOutput schema / properties / defaults_applied

Added value: +{
+  "description": "Optional parameters that were not supplied, so their documented defaults were used.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

First observedv0.1.0Sep 30, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Although annotations already declare read-only/idempotent/non-destructive/closed-world, the description adds real behavioral context: pure arithmetic with no I/O or external calls, rounded to 2 decimals, no auth or rate limits, a documented error path for unknown methods or missing method-required parameters, and the shape of the returned assumptions/steps. This goes well 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

For a 19-parameter, nine-method tool the length is justified and the purpose/routing is front-loaded before parameter mechanics. It is a dense single block, however, and the per-method parameter list would read better structured, so it is efficient rather than exemplary.

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?

Output schema exists so return values need not be re-explained, yet the description still summarizes the return shape and documents failure behavior. Sibling routing, parameter conventions, and the error contract are all present, leaving nothing an agent needs to invoke it correctly.

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% (baseline 3), but the description adds value the schema does not encode: the per-method parameter mapping (ltv→arpu+gross_margin+churn_rate, cac→sales_marketing_expense+new_customers, etc.) and the grouping convention (only method required, omit the rest). It covers only five of the nine methods, leaving mrr, magic_number, rule_of_40, and cac_payback unmapped, so it stops short of a 5.

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 names the exact resource (SaaS unit economics and valuation), enumerates the specific metrics computed (LTV, CAC, MRR, ARR, NRR, magic number, Rule of 40, CAC payback, revenue multiple), and states that the `method` argument selects among them. It explicitly distinguishes itself from siblings valuation_marketplace, valuation_fintech, and valuation_core by domain.

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?

It states when to use it (subscription software), when not to (marketplace GMV → valuation_marketplace; payments/lending → valuation_fintech; pre-revenue company-level → valuation_core), and which parameters each method requires. Routing to alternatives is explicit rather than inferred.

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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