Startup Valuation MCP Server

SaaS Metrics & Valuation

valuation_saas

Read-onlyIdempotent

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').

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 30, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the annotations (readOnly/idempotent/openWorld=false), the description discloses that this is pure arithmetic with no I/O or external calls, results are rounded to 2 decimals, there are no auth or rate limits, and that an unknown method or missing method-required parameter returns an error rather than a value. The error semantics are the most valuable addition since annotations cannot express them.

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?

Content is front-loaded correctly (metrics, then routing, then per-method params, then behavior) and every sentence carries weight given 19 parameters. It is delivered as one dense paragraph, which slightly hurts scannability, keeping it short of a 5.

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 19-parameter, enum-driven calculator with an output schema present, the description covers selection, sibling routing, parameter binding, numeric conventions, error behavior, and precision. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 100%, the description adds the crucial method-to-parameter dependency map (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) plus the convention that rates are fractions and non-selected params should be omitted. That dependency logic is not encoded in 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 names a specific resource (SaaS unit economics and valuation) and enumerates the exact metrics computed — LTV, CAC, MRR, ARR, NRR, magic number, Rule of 40, CAC payback, revenue multiple. It explicitly distinguishes itself from valuation_marketplace, valuation_fintech, and valuation_core, so an agent can route correctly without opening any schema.

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 gives explicit when-to-use ('for subscription software') plus explicit when-not with named alternatives ('for marketplace GMV metrics use valuation_marketplace', 'payments/lending use valuation_fintech', 'pre-revenue company-level value use valuation_core'). This is the full when/when-not/alternative pattern.

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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