financeanalytics-data

HowRisky

by howrisky

HowRisky: Financial risk analysis with Monte Carlo simulations and fat-tail modeling for portfolios, startup valuation,

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.

github stars

1

Institutional-grade KDE algorithms100 free API calls monthly8 specialized financial tools

best for

  • / Portfolio managers analyzing downside risk
  • / Startups modeling equity scenarios
  • / Real estate investors evaluating deals
  • / Quantitative analysts building risk models

capabilities

  • / 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

what it does

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.

about

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

NOASSERTION

HowRisky is released under the NOASSERTION license.

readme

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

<details> <summary>Claude Desktop</summary>

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?"

</details> <details> <summary>ChatGPT Desktop</summary>
  1. Open ChatGPT Desktop Settings
  2. Go to Apps & ConnectorsAdvanced settings
  3. Enable Developer mode
  4. Add MCP server configuration (use standard config above)

Restart ChatGPT Desktop.

Test it: Ask ChatGPT "Use HowRisky to calculate CVaR for 100% SPY portfolio"

</details> <details> <summary>Cursor</summary>

Add to Cursor's MCP configuration file:

Use the standard config above.

Cursor supports MCP via VS Code extension compatibility.

</details> <details> <summary>Windsurf</summary>

Add to Windsurf MCP settings:

Use the standard config above.

Windsurf's MCP integration works similarly to Cursor.

</details> <details> <summary>Cline (VS Code)</summary>

Via Cline MCP Marketplace:

  1. Open Cline in VS Code
  2. Search for "howrisky" in MCP Marketplace
  3. Click Install
  4. Enter API key when prompted

Manual Setup:

Add to VS Code Settings → Extensions → Cline → MCP Servers:

Use the standard config above.

</details> <details> <summary>GitHub Copilot / VS Code</summary>

Add to VS Code settings.json:

Use the standard config above in the MCP servers configuration section.

</details> <details> <summary>Remote Server (HTTP)</summary>

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}'
</details>

Available Tools

ToolDescription
calculate_portfolio_riskCVaR, VaR, ruin probability, survival probability
simulate_future_timelinesYear-by-year portfolio evolution with percentiles
compare_portfoliosSide-by-side risk comparison of multiple portfolios
text_to_portfolioNatural language → asset allocations
add_startupStartup equity modeling with exit scenarios
add_real_estateReal estate with cash flows, IRR, mortgage analysis
add_private_assetIlliquid asset modeling (PE funds, etc.)
add_gambleKelly 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:

  1. Discover HowRisky tools via tools/list
  2. Call calculate_portfolio_risk with correct parameters
  3. 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

TierCalls/MonthPrice
Free100$0
Developer10,000$99
Professional100,000$299
Enterprise1,000,000$999

View pricing: https://howrisky.ai/mcp/pricing


Support


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.

FAQ

What is the HowRisky MCP server?
HowRisky is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for HowRisky?
This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
MCP server reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

    HowRisky is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Piyush G· Sep 9, 2024

    We evaluated HowRisky against two servers with overlapping tools; this profile had the clearer scope statement.

  • Chaitanya Patil· Aug 8, 2024

    Useful MCP listing: HowRisky is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Sakshi Patil· Jul 7, 2024

    HowRisky reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Ganesh Mohane· Jun 6, 2024

    I recommend HowRisky for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Oshnikdeep· May 5, 2024

    Strong directory entry: HowRisky surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Dhruvi Jain· Apr 4, 2024

    HowRisky has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Rahul Santra· Mar 3, 2024

    According to our notes, HowRisky benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Pratham Ware· Feb 2, 2024

    We wired HowRisky into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Yash Thakker· Jan 1, 2024

    HowRisky is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.