Startup Valuation MCP Server

com.ascent-partners由 ascent-partners.com 发布官方发布者v2.1.4更新于 Nov 4, 2026

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

发布方自己的服务:Coso Mcp

已验证Streamable HTTP可网页运行金融数据与分析

概览

AI 生成的概览

计算创业公司估值:14 个工具覆盖 80 多个早期、行业与股权公式,可本地运行或使用托管端点。

可以做什么
  • 计算概率加权期望值、现值、净现值、现金流折现与复合年增长率
  • 用 CAPM、创业调整 CAPM、组合贝塔与 WACC 估算资本成本
  • 应用早期方法(评分卡、Berkus、风险因素、VC 法)以及期权与情景分析
  • 从可比公司推导市盈率、市销率、EV/EBITDA、EV/收入等倍数
  • 评估行业模型:SaaS 单位经济、市场平台、金融科技、生物医药 rNPV、硬件技术成熟度
  • 做跨境风险调整、在股东与股权类别间分配价值,并处理 SAFE、代币、ESG 与网络价值
适用场景
当助手需要为创业公司或融资轮次给出结构化、可审计的估值数字时适用,从早期评分卡到行业专用模型和股权稀释。适合希望得到带明确假设的公式化结果、而非自由估算的分析师和创始人。
运行要求
可使用托管的 Streamable HTTP 端点(无需安装、无需 API 密钥),或本地安装带 mcp 附加项的 startup-valuation Python 包,通过控制台脚本、python -m 或 uvx 启动。未声明任何环境变量或请求头。
安装前请注意
结果是基于所提供输入的模型输出,用于决策前应核对假设与参数。托管端点会接收发送给它的估值输入数据。不涉及凭据、付款或写入操作。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Startup Valuation MCP Server,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/com.ascent-partners%2Fstartup-valuation/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "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.

来源: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.

输入参数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.

输入参数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.

输入参数betabetasmethod必填weightstax_ratedebt_valuecost_of_debtequity_valuesize_premiummarket_returncost_of_equityrisk_free_rate另有 2 个

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.

输入参数method必填scoresweightsmultipleprototypeinvestmentpost_moneysound_ideaquality_teamrisk_ratingstarget_returnbase_valuation另有 5 个

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.

输入参数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.

输入参数stageebitdamethod必填revenuegeographyinterceptmarket_capnet_incomegrowth_ratemarket_maturityenterprise_valuestage_coefficient另有 3 个

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.

输入参数arrcacarpumethod必填arr_valuechurn_rategrowth_ratenet_new_arrgross_marginnew_customersprofit_marginending_revenue另有 7 个

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.

输入参数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.

输入参数roearpuyearsmethod必填customersloan_booktake_ratechurn_rategrowth_ratepe_multiplegross_marginnpl_reserves另有 3 个

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.

输入参数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.

输入参数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.

输入参数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.

输入参数cashyearsassetsmethod必填sharestax_rateequipmentinventoryprob_costscenariosinvestmentpost_money另有 21 个

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.

输入参数kncapratemethod必填supplydiscountvelocityesg_scoreinvestmentmarket_capdata_volume另有 18 个

版本历史

1
  1. v2.1.4最新Oct 14, 2026

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