startup-valuation

Fintech Valuation

valuation_fintech

Read-onlyIdempotent

Calculate payment revenue, lending valuation, payment-processor DCF, and neobank customer-based valuation by selecting the fintech model and entering required inputs.

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; other parameters are method-dependent, so supply those named for the selected method and omit the rest (documented defaults apply where defined). Returns an object with value, method, inputs, assumptions, chapter, formula_number and calculation steps. Pure arithmetic: no I/O and no external calls, and numeric results are returned rounded to 2 decimals. No authentication, credentials, or rate limits apply. Supplying an unknown method, or leaving unset a parameter that the chosen method requires, 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.
`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').

Schema Changelog

Changes observed during successful MCP inspections.

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/openWorld=false, and the description layers on context those annotations cannot carry: pure arithmetic with no I/O or external calls, results rounded to 2 decimals, no auth/credentials/rate limits, and explicit failure behavior for unknown methods 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 the dense method/parameter mapping, and every clause is functional. Slight redundancy in enumerating return fields (value, method, inputs, assumptions, chapter, formula_number, steps) when an output schema already documents them.

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, four-mode calculator with all-null-optional schema, the description supplies mode gating, required-vs-optional semantics, error behavior, and precision policy. With an output schema present, the extra return-shape sentence is redundant but harmless; 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.

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real value the schema cannot express: which parameters each method consumes, that only 'method' is required, that unlisted parameters should be omitted, and that documented defaults apply. Minor gap: 'years' and 'npl_reserves' are not mapped to any method even though lending/payment_processor formulas imply them.

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+resource ('Value and size fintech business models') and enumerates the four concrete model families it covers (payment revenue, lending, payment-processor DCF, neobank). It explicitly names the sibling it is not (valuation_saas), so an agent can route without opening either 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?

Gives an explicit routing rule ('Method selects the model. Use for payments, lending, and neobanks; for SaaS-style unit economics use valuation_saas'), naming the alternative and the condition that selects it. The method-parameter dependency table further tells the caller exactly what to supply per mode.

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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