Startup Valuation MCP Server

io.github.simonplmak-cloudv2.0.0Updated Sep 30, 2026

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

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Overview

AI-generated overview

Lets an assistant compute startup valuations using 80+ pre-revenue formulas, from scorecard and VC method to Black-Scholes and industry-specific models.

What it does
Exposes 14 MCP tools that fold 80+ valuation formulas from the Startup Valuation textbook, each tool grouping a family of methods behind a method argument. Covered families include 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. Every function returns a ValuationResult with value, assumptions, and sensitivity analysis, so results stay auditable. The same package also ships as a Python library and as AI-agent skill definitions.
When to use it
Useful when an assistant needs structured, auditable startup or pre-revenue company valuations, for example scorecard, Berkus, VC method, risk factor summation, option pricing, or industry-specific models such as SaaS LTV and CAC. Also relevant for scenario and Monte Carlo style analysis, dilution and liquidation preference questions, and SAFE or crypto valuation formulas.
Requirements
Runs either as a hosted Streamable HTTP endpoint at the provider's URL with no install and no API key, or locally via the PyPI package startup-valuation, installed with pip or run ephemerally with uvx, which needs Python. No environment variables or headers are declared.
Before you install
The server performs calculations only; it does not fetch live market data, so inputs and assumptions come from the caller and results should be treated as model output, not financial advice. The hosted endpoint sends the valuation inputs you supply to a third-party service. No credentials, payments, or write actions are declared.

Installation

In SourceWeft

  1. Open Startup Valuation MCP Server in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Other MCP clients

Add this to your client's mcpServers config.

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

README

Startup Valuation Engine

Comprehensive startup valuation library implementing 80+ formulas from the Startup Valuation textbook. Python library + MCP server + AI-Agent Skills.

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

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

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# 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.simonmak.com/api

OpenCode — add to opencode.json:

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

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

MCP Registry — published as io.github.simonplmak-cloud/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

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

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/simonplmak-cloud/startup-valuation},  license = {MIT},}

Based on formulas from the Startup Valuation textbook. See output/ for the full textbook source in markdown.

License

MIT — see LICENSE for details.

Source: README.md at commit 9823bc5

Tools

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Version history

1
  1. v2.0.0LatestSep 30, 2026