Startup Valuation MCP Server

Fintech Valuation

valuation_fintech

Read-onlyIdempotent

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.

First observedOct 3, 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, and closed-world. The description adds valuable context beyond annotations: pure arithmetic with no I/O or external calls, rounding to 2 decimals, no auth or rate limits, and explicit error behavior (unknown method or missing required parameter returns an error). This fully discloses the tool's operational behavior.

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?

The description is dense and front-loaded with purpose and usage, then method-specific parameters, then behavior. It is appropriately sized for a 15-parameter, 4-method tool. However, it lists return fields (value, method, inputs, assumptions, chapter, formula_number, calculation steps) even though an output schema exists, which is redundant and slightly reduces conciseness.

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?

Given the tool's complexity (15 parameters, 4 methods, output schema, annotations), the description covers method-dependent parameter requirements, input units, error handling, and operational behavior. With the output schema documenting return values, no critical information is missing for an agent to call 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%, but the description adds significant meaning by grouping parameters per method (e.g., lending needs loan_book + roe + pe_multiple). It also clarifies that inputs are fractions and that only method is required. A minor gap: the payment_processor wording 'adds growth_rate + discount_rate + terminal_multiple' could be clearer that it also requires the payment_revenue inputs (transaction_volume and take_rate).

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 sub-models it supports. It explicitly names the sibling valuation_saas as the tool for SaaS-style unit economics, enabling an agent to distinguish it from at least one alternative without opening 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?

Explicitly says when to use it: 'Use for payments, lending, and neobanks' and provides the alternative for SaaS-style unit economics ('use valuation_saas'). No inference is needed to select between this and the named sibling.

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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