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
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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>- 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"
</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:
- 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.
</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
| 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.
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 58 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.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
List & Promote Your MCP Server
Share your MCP server with the developer community
Ratings
4.5★★★★★58 reviews- ★★★★★Ganesh Mohane· Dec 28, 2024
HowRisky is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Amina Zhang· Dec 28, 2024
Useful MCP listing: HowRisky is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Isabella Bansal· Dec 16, 2024
HowRisky reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Isabella Agarwal· Dec 12, 2024
HowRisky is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Lucas Zhang· Dec 12, 2024
I recommend HowRisky for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Michael Srinivasan· Dec 8, 2024
According to our notes, HowRisky benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Yash Thakker· Nov 27, 2024
HowRisky has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Sakshi Patil· Nov 19, 2024
Useful MCP listing: HowRisky is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Lucas Liu· Nov 19, 2024
HowRisky is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Amina Liu· Nov 15, 2024
According to our notes, HowRisky benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
showing 1-10 of 58