startup-valuation

Fintech Valuation

valuation_fintech

Read-onlyIdempotent

Value fintech business models: payment revenue, lending, payment-processor DCF, and neobank customer LTV. Pick a method, supply its inputs, and get valuation plus steps.

Instructions

Value and size fintech business models: payment revenue, lending valuation, payment-processor DCF, and neobank customer-based valuation. Method selects the model. Use for payments, lending, and neobanks; for SaaS-style unit economics use valuation_saas. Parameters apply per method: payment_revenue needs transaction_volume + take_rate; lending needs loan_book + roe + pe_multiple; payment_processor adds growth_rate + discount_rate + terminal_multiple; neobank needs customers + arpu + gross_margin + churn_rate + pe_multiple. 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
`roe`	No	Return on equity as a decimal (0.20 = 20%).
`arpu`	No	Average revenue per user per month, currency units.
`years`	No	Forecast horizon in years; integer ≥ 1.
`method`	Yes	Formula to apply. Options: payment_revenue = Revenue = volume × take rate.; lending = V = loan book × ROE × P/E - NPL reserves.; payment_processor = DCF of payment revenue with a terminal multiple.; neobank = Customer LTV × P/E applied to the customer base.
`customers`	No	Number of customers.
`loan_book`	No	Outstanding loan book / principal, currency units.
`take_rate`	No	Take rate as a decimal (0.15 = 15% of GMV).
`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%).
`pe_multiple`	No	Price/earnings multiple applied to earnings.
`gross_margin`	No	Gross margin as a decimal (0.80 = 80%).
`npl_reserves`	No	Non-performing loan reserves deducted, currency units.
`discount_rate`	No	Discount rate as a decimal (0.12 = 12%).
`terminal_multiple`	No	Terminal value multiple applied at the horizon.
`transaction_volume`	No	Total payment transaction volume, 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.

Changed2 schema fields changedv2.1.1Oct 2, 2026

changedInput schema / properties / years / description

Previous value: -"Forecast horizon in years."New value: +"Forecast horizon in years; integer ≥ 1."

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?

Annotations already declare readOnly/idempotent/non-destructive, but the description adds substantial behavior beyond them: pure arithmetic with no I/O or external calls, results rounded to 2 decimals, no auth or rate limits, and explicit failure behavior on unknown method or missing method-required parameters.

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?

Front-loaded with purpose and routing before diving into the parameter matrix, and every clause is informative. It is long and has minor redundancy ('all other parameters are method-dependent, so supply those the selected method names and omit the rest'), but nothing is filler.

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 15-parameter, 4-mode tool with an output schema present, the description covers routing, per-method inputs, unit conventions, error behavior, and a brief return-value summary without duplicating the output schema in detail. An agent has everything needed 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%, so per-parameter meaning is already documented. The description still adds real value the schema cannot express: a method-to-required-parameter matrix (e.g. lending needs loan_book + roe + pe_multiple; neobank needs customers + arpu + gross_margin + churn_rate + pe_multiple) plus unit conventions (fractions, probability lists summing to 1).

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?

States a specific verb and resource ('Value and size fintech business models') and enumerates the four sub-models it covers (payment revenue, lending, processor DCF, neobank). It also names the sibling it is not (valuation_saas), so an agent can distinguish it without opening 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?

Explicit routing guidance: 'Use for payments, lending, and neobanks; for SaaS-style unit economics use valuation_saas.' It also explains the selection mechanism (method selects the model) and the exclusion condition (missing/unknown method returns an error rather than a value).

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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