startup-valuation
README.md
# Startup Valuation Engine
> Comprehensive startup valuation library implementing **80+ formulas** from the Startup Valuation textbook. Python library + MCP server + AI-Agent Skills.
[](https://pypi.org/project/startup-valuation/)
[](https://github.com/simonplmak-cloud/startup-valuation/actions/workflows/ci.yml)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://simonplmak-cloud.github.io/startup-valuation/)
[](https://github.com/simonplmak-cloud/startup-valuation/actions/workflows/ci.yml)
[](https://scorecard.dev/viewer/?uri=github.com/simonplmak-cloud/startup-valuation)
[](https://glama.ai/mcp/servers/simonplmak-cloud/startup-valuation)
[](https://startup-valuation.simonmak.com/api)
## Overview
A production-grade Python library for startup valuation, implementing every formula from the **[Startup Valuation](https://www.amazon.com/Startup-Valuation-Comprehensive-Fast-Growing-Pre-Revenue-ebook/dp/B0FYTGNVWS/)** 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 only
pip install startup-valuation[mcp] # + MCP server
pip install startup-valuation[dev] # + pytest, ruff, mypy
```
## Quick Start
### Python Library
```python
from startup_valuation.core import scorecard_valuation, vc_method_post_money
from startup_valuation.advanced import black_scholes, scenario_analysis
from 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 Analysis
scenarios = [
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]"
python mcp_server/server.py
```
**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`](server.json)) and listed on
[Glama](https://glama.ai/mcp/servers/simonplmak-cloud/startup-valuation) and the
[Official MCP Registry](https://registry.modelcontextprotocol.io). The
[`glama.json`](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
| Category | Methods | Chapter |
| ----------------- | -------------------------------------------------- | ------- |
| **Probability** | Expected value, joint probability, Poisson | 2 |
| **Time Value** | PV, NPV, annuity | 2 |
| **CAPM** | CAPM, portfolio beta, startup-adjusted | 2 |
| **Core** | Scorecard, Berkus, Risk Factor, VC Method | 3 |
| **Advanced** | Black-Scholes, Binomial, Monte Carlo, Scenario | 4 |
| **Comparables** | P/E, P/S, EV/EBITDA, regression-adjusted | 5 |
| **SaaS** | LTV, CAC, NRR, Magic Number, Rule of 40 | 11 |
| **Biotech** | rNPV, decision tree, peak sales, pipeline | 11 |
| **Fintech** | Payment revenue, lending, neobank, network effects | 11 |
| **Marketplace** | GMV, take rate, liquidity, network density | 11 |
| **Hardware** | TRL-adjusted, break-even, P-weighted DCF | 11 |
| **International** | PPP, CRP, currency-adjusted DCF, Damodaran | 12 |
| **Stakeholders** | Dilution, OPM, PWERM, liquidation, synergies | 13 |
| **Emerging** | SAFE, MV=PQ, ESG, Metcalfe's, data moat | 14 |
## 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 dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Run with coverage
pytest --cov=startup_valuation --cov-report=term-missing
# Lint
ruff check .
# Type check
mypy src/startup_valuation --ignore-missing-imports
```
## Documentation
- **API Reference:** [GitHub Pages](https://simonplmak-cloud.github.io/startup-valuation/)
- **Wiki (Theory & Derivations):** [GitHub Wiki](https://github.com/simonplmak-cloud/startup-valuation/wiki)
- **PyPI:** [pypi.org/project/startup-valuation](https://pypi.org/project/startup-valuation/)
- **Chapter Index:** Maps every function to its textbook chapter
- **Examples:** Interactive code snippets for each valuation category
## Companion Textbook
**[Startup Valuation: A Comprehensive Guide to Valuing Fast-Growing Pre-Revenue Companies](https://www.amazon.com/Startup-Valuation-Comprehensive-Fast-Growing-Pre-Revenue-ebook/dp/B0FYTGNVWS/)**
_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](LICENSE) for details.
TDQS
A4.8/5.0
Scored across 14 tools
Disambiguation5/5
Each tool targets a clearly distinct valuation subdomain (time value, CAPM, probability, core pre-revenue, advanced options/scenarios, comparables, vertical-specific models, international, stakeholder allocation, emerging methods). Descriptions explicitly route overlapping areas, such as probability vs. scenario analysis and core vs. emerging methods, reducing misselection risk.
Naming Consistency5/5
All 14 tools use a consistent snake_case naming pattern with the same valuation_ prefix. No mixed conventions or inconsistent verb styles are present.
Tool Count5/5
Fourteen tools is well within the ideal range and each tool clearly earns its place by covering a distinct family of valuation formulas or metrics. The set is neither too thin nor too heavy for a comprehensive startup-valuation calculator suite.
Completeness5/5
The surface covers a broad valuation lifecycle: time value, discount rates, probability, pre-revenue methods, advanced options/scenarios, comparables, vertical-specific models, international adjustments, stakeholder allocation, and emerging methods. No obvious gaps remain for the stated domain of startup valuation arithmetic.
Maintenance
ActivityMaintained
ResponsivenessNo issues