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

pip install startup-valuation          # library only
pip install startup-valuation[mcp]     # + MCP server
pip install startup-valuation[dev]     # + pytest, ruff, mypy

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

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 guided
prompts (`value_pre_revenue_startup`, `value_saas_startup`, `model_funding_round`)
and a machine-readable method catalog at `startup-valuation://methods`, so agents
can 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

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

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

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)"]

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"]

# 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

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

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