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Glama

Emerging & Alternative Methods

valuation_emerging
Read-onlyIdempotent

Value startups with SAFEs, tokens, ESG factors, network effects, data moats, or remote-first models. Select method to compute alternative valuations.

Instructions

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; 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
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 observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover read-only, idempotent, closed-world, non-destructive traits. The description adds beyond that: pure arithmetic with no I/O or external calls, rounding to 2 decimals, no authentication/credentials/rate limits, and explicit error behavior for unknown methods or missing 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?

Dense but appropriately sized for a 30-parameter multi-method tool, with purpose front-loaded. Some redundancy: the routing to valuation_core is stated twice, and return values are listed despite an output schema existing, adding minor bloat.

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 high complexity, 100% schema coverage, and an output schema, the description is complete: it covers routing, method-parameter mapping, error conditions, and operational behavior. An agent has everything needed to select and invoke the tool 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 description coverage is 100%, so baseline is 3. The description adds substantial value by mapping parameters to each method (e.g., safe_discount needs series_a_price + discount; metcalfe needs n; data_moat needs data_volume + data_uniqueness + monetization_rate + competitive_advantage_years). However, the mapping is incomplete for some methods (nvt_ratio, remote_npv, remote_premium, and precise esg_rate/esg_premium/esg_discount parameter sets are not spelled out).

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 (valuation methods) and enumerates the alternative approaches covered (SAFE, token, ESG, Metcalfe, data moat, remote-first). Explicitly names sibling tools (valuation_core, valuation_advanced, valuation_comparables) to distinguish scope.

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 clear when-to-use routing: 'Use for SAFEs, tokens, ESG...; for classic pre-revenue methods use valuation_core' and further routes to valuation_advanced and valuation_comparables. Leaves no ambiguity about which sibling to pick for a given scenario.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.