Startup Valuation MCP Server

com.ascent-partners由 ascent-partners.com 發布官方發布者v2.1.4更新於 Nov 4, 2026

Startup valuation for AI agents: 14 tools, 80+ pre-revenue formulas.

發布方自己的服務:Coso Mcp

已驗證Streamable HTTP可網頁執行金融資料與分析

概覽

AI 產生的概覽

計算新創公司估值:14 個工具涵蓋 80 多個早期、產業與股權公式,可本機執行或使用託管端點。

可以做什麼
  • 計算機率加權期望值、現值、淨現值、現金流折現與複合年成長率
  • 以 CAPM、新創調整 CAPM、投資組合貝他與 WACC 估算資金成本
  • 套用早期方法(計分卡、Berkus、風險因素、VC 法)以及選擇權與情境分析
  • 從可比公司推導本益比、股價營收比、EV/EBITDA、EV/營收等倍數
  • 評估產業模型:SaaS 單位經濟、交易平台、金融科技、生技 rNPV、硬體技術成熟度
  • 進行跨境風險調整、在股東與股權類別間分配價值,並處理 SAFE、代幣、ESG 與網路價值
適用情境
當助理需要為新創公司或募資輪次產出結構化、可稽核的估值數字時適用,從早期計分卡到產業專用模型與股權稀釋。適合希望取得附明確假設的公式化結果、而非自由估算的分析師與創辦人。
執行需求
可使用託管的 Streamable HTTP 端點(免安裝、免 API 金鑰),或在本機安裝含 mcp 附加項的 startup-valuation Python 套件,透過主控台指令、python -m 或 uvx 啟動。未宣告任何環境變數或標頭。
安裝前請注意
結果是依所提供輸入產生的模型輸出,用於決策前應核對假設與參數。託管端點會接收傳送給它的估值輸入資料。不涉及憑證、付款或寫入操作。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Startup Valuation MCP Server,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/com.ascent-partners%2Fstartup-valuation/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "startup-valuation": {
      "type": "http",
      "url": "https://startup-valuation.ascent-partners.com/api"
    }
  }
}

README

Startup Valuation Engine

A comprehensive startup valuation library implementing 80+ formulas from the Startup Valuation textbook — Python library, MCP server, and AI-agent skills.

[CI] [MCP Registry] [Glama] [MCP tools] [PyPI] [Python 3.10+] [Coverage] [Docs] [OpenSSF Scorecard] [License: MIT]

Overview

A production-grade Python library for startup valuation, implementing every formula from the Startup Valuation textbook by Simon Mak (Valuation in Practice Series, Ascent Partners). Designed for developers, financial analysts, and AI agents who need auditable, structured valuation computations.

Three-layer architecture:

mermaid
graph TB    subgraph Library["Python Library"]        MOD["14 Modules<br/>80+ Functions"] --> VR["ValuationResult"]    end    subgraph MCP["MCP Server"]        VR --> SVR["FastMCP Server<br/>14 Tools"]    end    subgraph Skills["AI-agent skills"]        SVR --> CORE["Core"]        SVR --> ADV["Advanced"]        SVR --> IND["Industry"]        SVR --> STAKE["Stakeholder"]        SVR --> EMER["Emerging"]    end    style Library fill:#0083AB,color:#fff    style MCP fill:#4CAF50,color:#fff    style Skills fill:#9C27B0,color:#fff
  1. Python Library — 14 modules, 80+ typed functions, all returning ValuationResult (value + assumptions + sensitivity)
  2. MCP Server — 14 folded tools (80+ formulas) for AI agents via stdio and hosted Streamable HTTP
  3. AI-agent skills — 6 skill definitions with workflow guidance for valuation domains

Installation

bash
pip install startup-valuation          # library onlypip install startup-valuation[mcp]     # + MCP serverpip install startup-valuation[dev]     # + pytest, ruff, mypy

Quick Start (≤ 5 minutes)

Fastest path: one line — pip install "startup-valuation[mcp]" — then call any *_valuation tool (Scorecard, VC method, Black-Scholes, scenario analysis).

Python Library

python
from startup_valuation.core import scorecard_valuation, vc_method_post_moneyfrom startup_valuation.advanced import black_scholes, scenario_analysisfrom startup_valuation.types import Scenario
# Scorecard Method (pre-revenue startups)result = scorecard_valuation(    average_valuation=1_500_000,    weights=[0.30, 0.25, 0.15, 0.10, 0.10, 0.05, 0.05],    scores=[1.25, 1.50, 1.20, 0.75, 1.00, 0.90, 1.00],)print(f"Scorecard: ${result.value:,.0f}")  # $1,800,000
# Black-Scholes for real options (startup equity)result = black_scholes(    underlying=20_000_000, strike=5_000_000,    risk_free_rate=0.05, volatility=0.40, time_to_maturity=1.0,)print(f"Option value: ${result.value:,.0f}")  # $15,240,000
# Scenario Analysisscenarios = [    Scenario("bull", 0.20, 10_000_000),    Scenario("base", 0.60, 5_000_000),    Scenario("bear", 0.20, 1_000_000),]result = scenario_analysis(scenarios)print(f"Expected value: ${result.value:,.0f}")  # $5,200,000

MCP Server (for AI Agents)

The server exposes 14 tools, each folding a family of formulas behind a method argument — probability, time value, CAPM, core pre-revenue methods, options, comparables, SaaS, marketplaces, fintech, biotech, hardware, international, stakeholder equity, emerging methods, and a triangulated full analysis.

Local (stdio):

bash
pip install "startup-valuation[mcp]"startup-valuation-mcp          # console script installed with the [mcp] extra
**Prompts and resources.** Besides the 14 tools, the server offers three guidedprompts (`value_pre_revenue_startup`, `value_saas_startup`, `model_funding_round`)and a machine-readable method catalog at `startup-valuation://methods`, so agentscan see every method's required parameters before calling a tool.
# or: python -m startup_valuation.mcp# or ephemeral, no clone: uvx --from startup-valuation startup-valuation-mcp

Hosted (Streamable HTTP) — no install, no API key:

https://startup-valuation.ascent-partners.com/api

OpenCode — add to opencode.json:

json
"startup-valuation": {  "type": "remote",  "url": "https://startup-valuation.ascent-partners.com/api",  "timeout": 60000}

Claude Desktop / Cursor — add the HTTP URL https://startup-valuation.ascent-partners.com/api as an MCP server, or run the stdio entrypoint above.

MCP Registry — published as com.ascent-partners/startup-valuation (manifest: server.json) and listed on Glama and the Official MCP Registry. The glama.json file holds the Glama maintainer entry.

AI-agent skills

Copy the skills/ directory to your agent's skills folder:

  • valuation-core — Scorecard, Berkus, VC Method, Risk Factor Summation
  • valuation-foundations — Probability, time value, CAPM, comparables
  • valuation-advanced — Black-Scholes, Binomial, Monte Carlo, Scenario Analysis
  • valuation-industry — SaaS, Biotech, Fintech, Marketplace, Hardware
  • valuation-stakeholder — Dilution, OPM, PWERM, Liquidation Preference
  • valuation-emerging — SAFE, Crypto (MV=PQ), ESG, Metcalfe's Law

Valuation Methods by Category

CategoryMethodsChapter
ProbabilityExpected value, joint probability, Poisson2
Time ValuePV, NPV, annuity2
CAPMCAPM, portfolio beta, startup-adjusted2
CoreScorecard, Berkus, Risk Factor, VC Method3
AdvancedBlack-Scholes, Binomial, Monte Carlo, Scenario4
ComparablesP/E, P/S, EV/EBITDA, regression-adjusted5
SaaSLTV, CAC, NRR, Magic Number, Rule of 4011
BiotechrNPV, decision tree, peak sales, pipeline11
FintechPayment revenue, lending, neobank, network effects11
MarketplaceGMV, take rate, liquidity, network density11
HardwareTRL-adjusted, break-even, P-weighted DCF11
InternationalPPP, CRP, currency-adjusted DCF, Damodaran12
StakeholdersDilution, OPM, PWERM, liquidation, synergies13
EmergingSAFE, MV=PQ, ESG, Metcalfe's, data moat14

The valuation ladder

mermaid
flowchart LR    IDEA["Idea / pre-revenue"] --> SC["Scorecard · Berkus · Risk Factor"]    SC --> VC["VC Method · OPM · PWERM"]    VC --> ADV["Black-Scholes · Binomial · Monte Carlo · Scenario"]    ADV --> COMP["Comparables: P/E · P/S · EV/EBITDA"]    COMP --> IND["Industry: SaaS · Biotech · Fintech · Marketplace · Hardware"]    IND --> R["ValuationResult (value · assumptions · sensitivity)"]

Distribution

mermaid
flowchart LR    PKG["startup-valuation<br/>14 modules · 80+ functions"] --> L["pip install startup-valuation"]    PKG --> MP["pip install startup-valuation[mcp]<br/>→ 14-tool MCP server"]    MP --> T1["stdio (local)"]    MP --> T2["hosted Streamable HTTP"]

Why This Library?

  • Auditable — Every function returns ValuationResult with value, method, inputs, assumptions, and sensitivity analysis
  • Textbook-accurate — All formulas verified against book example values with unit tests
  • AI-ready — MCP server and Skills for seamless AI agent integration
  • Industry-specific — Dedicated modules for SaaS, biotech, fintech, marketplace, and hardware startups
  • Open source — MIT license, extensible, well-documented

Development

bash
# Install dev dependenciespip install -e ".[dev]"
# Run testspytest
# Run with coveragepytest --cov=startup_valuation --cov-report=term-missing
# Lintruff check .
# Type checkmypy src/startup_valuation --ignore-missing-imports

Note: CI and deployment run through the APG workbench pipeline.

Documentation

Companion Textbook

Startup Valuation: A Comprehensive Guide to Valuing Fast-Growing Pre-Revenue Companies
Theory, Methods, Regulation, and Practice — Valuation in Practice Series by Ascent Partners
By Simon Mak · 338 pages · 15 chapters · 300+ exercises · 20+ real-world cases

Citing This Project

bibtex
@software{startup_valuation_engine,  author = {Mak, Simon},  title = {Startup Valuation Engine},  year = {2026},  url = {https://github.com/simonmak-ascent/startup-valuation},  license = {MIT},}

Based on formulas from the Startup Valuation textbook.

Use with Context7

Up-to-date Startup Valuation Engine documentation is indexed on Context7, so coding agents can pull it into context on demand. With the Context7 MCP server or ctx7 CLI installed, name the library in your prompt:

text
use library /simonmak-ascent/startup-valuation for API and docs

License

MIT — see LICENSE.


By Ascent Partners — part of the Valuation in Practice Series.

If this saves you time, a ⭐ on GitHub helps others find it.

來源:README.md,提交 78d4907

工具

14

valuation_probability

Probability & Expected Value

Compute expected value and probability-weighted outcomes for startup scenarios: discrete E[X], joint probability of sequential events, probability-weighted value, VC portfolio expected return, Poisson event probability, and continuous E[X] over a range. Method selects the formula. Use for probability-weighted central estimates; for named bull/base/bear tables or option pricing use valuation_advanced, and to discount cash flows use valuation_time_value. Parameters apply per method: expected_value_discrete and probability_weighted need outcomes + probabilities; portfolio_return needs weights + returns; poisson needs mean_events + k; expected_value_continuous needs lower + upper. outcomes and probabilities must be equal length, and the probabilities should sum to 1. Routing: use valuation_advanced method 'scenario_analysis' for named bull/base/bear scenario tables, and its black_scholes/binomial methods for option pricing; use this tool for arbitrary outcome lists and probability-weighted central estimates. 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.

輸入參數kloweruppermethod必填returnsweightsoutcomesmean_eventsprobabilities

valuation_time_value

Time Value of Money

Discount, compound, and forecast value over time: single future value PV, net present value of a cash-flow stream, annuity present value, discounted cash flow with a Gordon terminal value, constant-rate compound growth of revenue or cash flow, and the implied compound annual growth rate (CAGR). Method selects the formula. Use to convert future cash to today's value, to value a full forecast with a terminal value (dcf), to project a revenue or cash-flow series forward, or to derive the growth rate implied by two values; get the discount rate from valuation_capm or valuation_international. Parameters apply per method: present_value needs future_value + rate + periods; npv needs cash_flows + rate; annuity needs payment + rate + periods; dcf needs cash_flows + rate (optional: terminal_growth); compound_growth needs starting_value + growth_rate + periods; cagr needs starting_value + ending_value + periods. growth_rate must be greater than -1, cagr requires starting_value > 0 and periods > 0, and dcf requires rate greater than terminal_growth. Not for option values (use valuation_advanced) or for expected values over outcomes (use valuation_probability). 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.

輸入參數ratemethod必填paymentperiodscash_flowsgrowth_rateending_valuefuture_valuestarting_valueterminal_growth

valuation_capm

CAPM & Cost of Equity

Estimate the cost of capital: standard CAPM, startup-adjusted CAPM with size and illiquidity premiums, portfolio beta from weighted asset betas, and WACC blending after-tax cost of equity and debt. Method selects the formula. Use to derive the discount rate that feeds valuation_time_value and DCF models; for cross-border rates add valuation_international. Parameters apply per method: capm needs risk_free_rate + beta + market_return; startup_capm adds size_premium and liquidity_premium; portfolio_beta needs weights + betas, which must be equal length; wacc needs equity_value + debt_value + cost_of_equity + cost_of_debt + tax_rate. 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.

輸入參數betabetasmethod必填weightstax_ratedebt_valuecost_of_debtequity_valuesize_premiummarket_returncost_of_equityrisk_free_rate另有 2 個

valuation_core

Pre-Revenue Core Methods

The textbook's pre-revenue methods: Scorecard, Berkus, Risk-Factor Summation, VC Method (post- and pre-money), and exit terminal value. Use these first for early-stage startups. Method selects the formula, and each method names its own parameters: scorecard needs average_valuation + weights + scores; berkus takes five factor awards; risk_factor needs base_valuation + risk_ratings; vc_post_money needs terminal_value + target_return; vc_pre_money needs post_money + investment; terminal_value needs projected_revenue + multiple; triangulated needs the scorecard inputs plus terminal_value/target_return/investment. Routing: for SAFEs, tokens, ESG, network effects, or data-moat methods use valuation_emerging; for options or bull/base/bear 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.

輸入參數method必填scoresweightsmultipleprototypeinvestmentpost_moneysound_ideaquality_teamrisk_ratingstarget_returnbase_valuation另有 5 個

valuation_advanced

Options & Scenario Analysis

Advanced techniques: Black-Scholes call value, binomial-tree option value, and scenario analysis. Method selects the technique. For a quick expected value over arbitrary outcome lists, prefer valuation_probability with method 'probability_weighted'; scenario_analysis here is for explicit named bull/base/bear scenario tables. Parameters apply per method: black_scholes and binomial need underlying + strike + risk_free_rate + volatility + time_to_maturity (binomial adds steps); scenario_analysis needs scenarios. Not for plain discounted cash flow — for that use valuation_time_value. 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.

輸入參數stepsmethod必填strikescenariosunderlyingvolatilityrisk_free_ratetime_to_maturity

valuation_comparables

Comparable Multiples

Market multiples from comparables: P/E, P/S, EV/EBITDA, EV/Revenue, and a regression-adjusted multiple. Method selects the ratio. Use when public comparables exist; for pre-revenue or private startups use valuation_core. Parameters apply per method: pe_ratio needs market_cap + net_income; ps_ratio needs market_cap + revenue; ev_ebitda needs enterprise_value + ebitda; ev_revenue needs enterprise_value + revenue; regression_multiple needs intercept + growth_rate + growth_coefficient (plus optional maturity/stage/geography terms). 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.

輸入參數stageebitdamethod必填revenuegeographyinterceptmarket_capnet_incomegrowth_ratemarket_maturityenterprise_valuestage_coefficient另有 3 個

valuation_saas

SaaS Metrics & Valuation

SaaS unit economics and valuation: LTV, CAC, MRR, ARR, net revenue retention, magic number, Rule of 40, CAC payback, and ARR revenue-multiple valuation. Method selects the metric. Use for subscription software; for marketplace GMV metrics use valuation_marketplace and for payments/lending use valuation_fintech. Parameters apply per method: ltv needs arpu + gross_margin + churn_rate; cac needs sales_marketing_expense + new_customers; arr needs subscription_values; nrr needs starting_revenue + ending_revenue; revenue_multiple needs arr + revenue_multiple. Not for company-level pre-revenue value — for that use valuation_core. 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.

輸入參數arrcacarpumethod必填arr_valuechurn_rategrowth_ratenet_new_arrgross_marginnew_customersprofit_marginending_revenue另有 7 個

valuation_marketplace

Marketplace Metrics

Marketplace health and valuation: take rate, GMV revenue-multiple valuation, buyer retention, and network density. Method selects the metric. Use for two-sided transaction marketplaces; for subscription software use valuation_saas. Parameters apply per method: take_rate needs revenue + gmv; gmv_multiple needs gmv + multiple; buyer_retention needs buyers_period_1 + buyers_repeat; network_density needs active_buyers + active_sellers + total_users. 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.

輸入參數gmvmethod必填revenuemultipletotal_usersactive_buyersbuyers_repeatactive_sellersbuyers_period_1

valuation_fintech

Fintech Valuation

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.

輸入參數roearpuyearsmethod必填customersloan_booktake_ratechurn_rategrowth_ratepe_multiplegross_marginnpl_reserves另有 3 個

valuation_biotech

Biotech Pipeline Valuation

Risk-adjusted biotech valuation: peak sales, decision-tree expected value, and full pipeline rNPV across drugs. Method selects the model. Use for pharma/drug pipelines; for hardware or deep tech use valuation_hardware. Parameters apply per method: peak_sales needs patient_population + penetration + price; decision_tree needs probabilities + terminal_value; pipeline needs drugs + discount_rate. Not for hardware or deep tech — for that use valuation_hardware. 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.

輸入參數drugspricemethod必填compliancepenetrationdiscount_rateprobabilitiesterminal_valuepatient_population

valuation_hardware

Hardware & Unit Economics

Hardware and deep-tech valuation: TRL-risk-adjusted valuation, gross margin, and break-even volume. Method selects the metric. Use for hardware and deep tech with technology-readiness risk; for drug pipelines use valuation_biotech. Parameters apply per method: trl needs market_size + market_share + margin + multiple + trl_discount; gross_margin needs asp + variable_cost; break_even_volume needs fixed_costs + asp + variable_cost. Not for drug pipelines — for those use valuation_biotech. 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.

輸入參數aspmarginmethod必填multiplefixed_costsmarket_sizemarket_sharetrl_discountvariable_cost

valuation_international

International Valuation

Cross-border adjustments: purchasing-power parity, country risk premium, and international CAPM. Method selects the adjustment. Use for cross-border cash flows and country risk; pair with valuation_capm and valuation_time_value. Parameters apply per method: ppp needs spot_rate + inflation_foreign + inflation_domestic; country_risk_premium needs sovereign_yield + us_treasury_yield; intl_capm needs risk_free_rate + beta + mrp + crp. Not for the domestic cost of equity — for that use valuation_capm. 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.

輸入參數crpmrpbetamethod必填spot_raterisk_free_ratesovereign_yieldinflation_foreignus_treasury_yieldinflation_domestic

valuation_stakeholder

Stakeholder & Equity Allocation

Allocate value across stakeholders and equity classes: single-round dilution, OPM common stock, PWERM, liquidation value, M&A synergy, employee-option values, vesting adjustment, cash-vs-equity break-even, and asset-based loan capacity. Method selects the model. Use only after the company-level value is known (from valuation_core, valuation_saas, or valuation_comparables) to split that value across the cap table; for the company value itself do not use this tool. Parameters apply per method: dilution needs ownership_before + investment + post_money; opm needs enterprise_value + liquidation_pref + time_to_exit + volatility; pwerm and employee_option need scenarios; liquidation needs assets + recovery_rates; risk_adjusted_synergy needs revenue_synergies + cost_synergies; vesting_adjusted needs total_value + vested_fraction; max_asset_loan takes collateral values. 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.

輸入參數cashyearsassetsmethod必填sharestax_rateequipmentinventoryprob_costscenariosinvestmentpost_money另有 21 個

valuation_emerging

Emerging & Alternative Methods

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.

輸入參數kncapratemethod必填supplydiscountvelocityesg_scoreinvestmentmarket_capdata_volume另有 18 個

版本歷史

1
  1. v2.1.4最新Oct 14, 2026

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