HowRisky
Provides Monte Carlo risk analysis and financial modeling with fat-tail distributions for portfolio analysis, startup valuations, and investment strategies. Uses institutional-grade algorithms to calculate risk metrics like CVaR and ruin probability.
Financial risk analysis with Monte Carlo simulations and fat-tail modeling for portfolio analysis, startup equity valuation, real estate investment analysis, and Kelly criterion betting strategies.
What it does
- Run Monte Carlo simulations on portfolios
- Calculate CVaR and ruin probabilities
- Analyze startup equity valuations
- Evaluate real estate investment risks
- Optimize Kelly criterion betting strategies
- Model fat-tail distributions for risk analysis
Best for
About HowRisky
HowRisky is a community-built MCP server published by howrisky that provides AI assistants with tools and capabilities via the Model Context Protocol. HowRisky: Financial risk analysis with Monte Carlo simulations and fat-tail modeling for portfolios, startup valuation, It is categorized under finance, analytics data.
How to install
You can install HowRisky in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
License
HowRisky is released under the NOASSERTION license.
HowRisky MCP Server
Monte Carlo risk analysis for AI agents. Institutional-grade financial modeling with fat-tail distributions and proprietary KDE algorithms.
8 Tools: Portfolio risk (CVaR, ruin probability), startup equity, real estate, Kelly criterion betting, and more.
Compatible with: Claude Desktop, ChatGPT Desktop, Cursor, Windsurf, Cline, GitHub Copilot, VS Code, Codex
Standard Config
{
"mcpServers": {
"howrisky": {
"command": "npx",
"args": ["-y", "howrisky-mcp-server"],
"env": {
"HOWRISKY_API_KEY": "your-api-key-here"
}
}
}
}
Get your free API key at: https://howrisky.ai/app/settings (100 calls/month free)
Getting Started
Step 1: Get your API key from https://howrisky.ai/app/settings
Step 2: Add the standard config above to your AI tool's MCP configuration
That's it! Your AI can now access Monte Carlo risk simulations.
Installation
Claude Desktop
Edit config file:
- MacOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
Add the standard config above.
Restart Claude Desktop.
Test it: Ask Claude "Using HowRisky, what's the risk of a 60/40 portfolio?"
ChatGPT Desktop
- Open ChatGPT Desktop Settings
- Go to Apps & Connectors → Advanced settings
- Enable Developer mode
- Add MCP server configuration (use standard config above)
Restart ChatGPT Desktop.
Test it: Ask ChatGPT "Use HowRisky to calculate CVaR for 100% SPY portfolio"
Cursor
Add to Cursor's MCP configuration file:
Use the standard config above.
Cursor supports MCP via VS Code extension compatibility.
Windsurf
Add to Windsurf MCP settings:
Use the standard config above.
Windsurf's MCP integration works similarly to Cursor.
Cline (VS Code)
Via Cline MCP Marketplace:
- Open Cline in VS Code
- Search for "howrisky" in MCP Marketplace
- Click Install
- Enter API key when prompted
Manual Setup:
Add to VS Code Settings → Extensions → Cline → MCP Servers:
Use the standard config above.
GitHub Copilot / VS Code
Add to VS Code settings.json:
Use the standard config above in the MCP servers configuration section.
Remote Server (HTTP)
For custom integrations or web-based AI tools:
Endpoint: https://mcp.howrisky.ai
Authentication: Include X-API-Key header with your API key
Documentation: https://howrisky.ai/mcp/docs
Example:
curl -X POST https://mcp.howrisky.ai \
-H "X-API-Key: your-api-key" \
-H "Content-Type: application/json" \
-d '{"jsonrpc":"2.0","method":"tools/list","id":1}'
Available Tools
| Tool | Description |
|---|---|
calculate_portfolio_risk | CVaR, VaR, ruin probability, survival probability |
simulate_future_timelines | Year-by-year portfolio evolution with percentiles |
compare_portfolios | Side-by-side risk comparison of multiple portfolios |
text_to_portfolio | Natural language → asset allocations |
add_startup | Startup equity modeling with exit scenarios |
add_real_estate | Real estate with cash flows, IRR, mortgage analysis |
add_private_asset | Illiquid asset modeling (PE funds, etc.) |
add_gamble | Kelly criterion for high-risk betting strategies |
Full documentation: https://howrisky.ai/mcp/docs
Example Usage
Once configured, ask your AI:
"Using HowRisky, calculate the risk of investing $100K in a 60/40 portfolio over 20 years"
The AI will:
- Discover HowRisky tools via
tools/list - Call
calculate_portfolio_riskwith correct parameters - Return CVaR, survival probability, ruin risk, and other metrics
Features
Fat-Tail Modeling - Gaussian models underestimate crash risk by 3-10x. Our proprietary KDE captures reality.
Comprehensive Metrics - 12 risk metrics including CVaR 95/99, VaR, ruin probability, percentiles
Private Assets - Model startups, real estate, PE funds, and high-risk gambles
Tax-Aware - 15+ countries supported (US, GB, DE, FR, IT, ES, JP, AU, CA, etc.)
Custom Scenarios - Override historical data with your own market assumptions
Pricing
| Tier | Calls/Month | Price |
|---|---|---|
| Free | 100 | $0 |
| Developer | 10,000 | $99 |
| Professional | 100,000 | $299 |
| Enterprise | 1,000,000 | $999 |
View pricing: https://howrisky.ai/mcp/pricing
Support
- Issues: https://github.com/howrisky/howrisky-mcp-server/issues
- Docs: https://howrisky.ai/mcp/docs
- Email: [email protected]
License
Proprietary - Copyright © 2025 Diogo Seca / HowRisky.ai
You may use this software to access HowRisky MCP API. Modification and redistribution prohibited. See LICENSE for details.
Alternatives
Related Skills
Browse all skillsTransform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent analysis
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.
Comprehensive personal finance management system for analyzing transaction data, generating insights, creating visualizations, and providing actionable financial recommendations. Use when users need to analyze spending patterns, track budgets, visualize financial data, extract transactions from PDFs, calculate savings rates, identify spending trends, generate financial reports, or receive personalized budget recommendations. Triggers include requests like "analyze my finances", "track my spending", "create a financial report", "extract transactions from PDF", "visualize my budget", "where is my money going", "financial insights", "spending breakdown", or any finance-related analysis tasks.
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.