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Startup Valuation MCP Server

by io.github.simonplmak-cloud
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Emerging & Alternative Methods

valuation_emerging
Read-onlyIdempotent

Modern and alternative valuation: SAFE conversion (discount, cap, expected value), token valuation (equation of exchange, NVT), ESG adjustments (rate, premium, discount), Metcalfe network value, data-moat value, and remote-first premium/NPV. Method selects the model. Use for SAFEs, tokens, ESG, network effects, data moats, and remote-first adjustments; for classic pre-revenue methods use valuation_core. Parameters apply per method: safe_discount needs series_a_price + discount; safe_cap needs cap + series_a_price; safe_expected needs investment + cap + discount + series_a_valuation + series_a_price; token_value needs transaction_volume + price_per_tx + velocity + supply; metcalfe needs n; esg_* need base_valuation + a score; data_moat needs data_volume + data_uniqueness + monetization_rate + competitive_advantage_years. Routing: for classic pre-revenue methods (Scorecard, Berkus, Risk-Factor Summation, VC Method) use valuation_core; for options or scenario tables use valuation_advanced; for public-comparable multiples use valuation_comparables. 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
NameRequiredDescriptionDefault
kNoNumber of events k for the Poisson probability P(X=k).
nNoNumber of users or nodes in the network.
capNoSAFE valuation cap, currency units.
rateNoPer-period discount rate as a decimal (0.10 = 10%).
methodYesFormula to apply. Options: safe_discount = Price = Series A price × (1 - discount).; safe_cap = Price = cap / pre-money shares (cap-based).; safe_expected = Expected SAFE value across cap and discount outcomes.; token_value = Value = (volume × price) / (velocity × supply).; nvt_ratio = NVT = market cap / daily transaction volume.; esg_rate = r = base + ESG risk premium - ESG opportunity discount.; esg_premium = Valuation uplift = base × (1 + score × premium per point).; esg_discount = Valuation reduction = base × (1 - risk score × discount per point).; metcalfe = V = k · n².; data_moat = Discounted value of monetised proprietary data.; remote_npv = Perpetuity NPV = annual savings / discount rate.; remote_premium = Valuation premium from cost savings, talent access, and productivity.
supplyNoCirculating token supply.
discountNoConversion discount as a decimal (0.20 = 20% discount).
velocityNoToken velocity (turnover of supply per period).
esg_scoreNoESG score in points (e.g. 0-100).
investmentNoAmount invested, currency units.
market_capNoMarket capitalisation, currency units.
data_volumeNoVolume of proprietary data held.
price_per_txNoProtocol revenue per transaction, currency units.
discount_rateNoDiscount rate as a decimal (0.12 = 12%).
annual_savingsNoAnnual cost savings, currency units.
base_valuationNoPre-adjustment baseline valuation, currency units.
esg_risk_scoreNoESG risk score in points (higher = riskier).
series_a_priceNoPrice per share in the next priced (Series A) round.
data_uniquenessNoUniqueness / scarcity of the data in [0,1].
cost_savings_pctNoCost savings as a fraction of baseline.
esg_risk_premiumNoESG risk premium added to the rate, as a decimal.
monetization_rateNoFraction of data value monetisable as a decimal.
premium_per_pointNoValuation premium per ESG point as a decimal.
productivity_gainNoProductivity gain as a decimal.
discount_per_pointNoValuation discount per ESG risk point as a decimal.
series_a_valuationNoSeries A post-money valuation, currency units.
transaction_volumeNoTotal payment transaction volume, currency units.
talent_access_premiumNoTalent-access premium as a decimal.
esg_opportunity_discountNoESG opportunity discount subtracted from the rate.
competitive_advantage_yearsNoYears the data moat is expected to last.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoError message when the call fails.
stepsNoIntermediate steps for traceability.
valueYesComputed valuation or metric.
inputsNoEcho of the normalised inputs used.
methodNoFormula / method name that produced the result.
chapterNoSource textbook chapter.
assumptionsNoModelling assumptions applied.
formula_numberNoSource textbook formula number (e.g. '3.1').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations cover safety (readOnly, idempotent, non-destructive), and the description adds genuinely new behavioral context beyond them: pure arithmetic with no I/O or external calls, results rounded to 2 decimals, no auth or rate limits, and error behavior for unknown methods or missing method-required params. This is exactly the extra context annotations cannot carry.

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 method enumeration, then routing, then per-method params, then semantics/return notes. Dense but every sentence is load-bearing; the only cost is length, which is justified by 30 parameters and 12 enum methods.

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 an output schema exists, the description needn't detail return values, yet it still notes the returned fields (value, method, inputs, assumptions, chapter, formula_number, steps). Combined with routing, per-method param requirements, and error behavior, an agent has everything needed to call this 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 the baseline is 3, but the description goes further by mapping parameters to each method (e.g. safe_expected needs investment + cap + discount + series_a_valuation + series_a_price; token_value needs volume + price + velocity + supply), which resolves the 30-param selection problem the flat schema cannot. It also states that only method is required and that rate/decimal inputs are fractions in [0,1], adding real semantic value.

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 domain (modern/alternative valuation) and enumerates the exact methods handled: SAFE conversion, token valuation, ESG adjustments, Metcalfe, data-moat, and remote-first. Coupled with the explicit routing sentence, an agent can distinguish this from valuation_core, valuation_advanced, and valuation_comparables 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?

Explicit when-to-use ('Use for SAFEs, tokens, ESG, network effects, data moats, and remote-first adjustments') plus explicit alternatives with conditions ('for classic pre-revenue methods use valuation_core; for options or scenario tables use valuation_advanced; for public-comparable multiples use valuation_comparables'). Nothing is left to inference.

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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