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QuantRisk

78degrees/mcp-server

  • 2 repo stars
  • Updated May 26, 2026
  • 78degrees/mcp-server

Portfolio risk analytics - VaR, Monte Carlo, optimization, options Greeks, stress testing.

About

Portfolio risk analytics - VaR, Monte Carlo, optimization, options Greeks, stress testing. npm install -g @quantrisk/mcp-server Add to your `claude_desktop_config.json`: Exposes 10 MCP tools including dev.quantrisk/mcp-server, QUANTRISK_API_KEY, | Tool | Description | Tier |, |------|-------------|------|. Install via Claude Desktop, Cursor, or any MCP-compatible client using the upstream server manifest.

  • **Institutional-grade portfolio risk analytics for Claude and any MCP client.**
  • **2. Configure** (Claude Desktop - see [below](#configuration) for Cursor)
  • **"Run a Monte Carlo simulation on my portfolio: 50% AAPL, 30% MSFT, 20% NVDA. Show me the 5th percentile outcome."**
  • **"Stress test 70% VTI / 30% BND against the 2008 financial crisis and a hypothetical 300bp rate shock."**
  • **"What's my sector exposure if I hold equal weights in AMZN, JPM, JNJ, XOM, and NEE?"**

QuantRisk by the numbers

  • Exposes 10 verified tools (MCP introspection)
  • Data as of Jul 9, 2026 (Skillselion catalog sync)
terminal
claude mcp add --env QUANTRISK_API_KEY=YOUR_QUANTRISK_API_KEY mcp-server -- npx -y @quantrisk/mcp-server

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Package@quantrisk/mcp-server
TransportSTDIO, HTTP
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Last updatedMay 26, 2026
Repository78degrees/mcp-server ↗

How do I connect QuantRisk to my MCP client?

Portfolio risk analytics - VaR, Monte Carlo, optimization, options Greeks, stress testing.

Who is it for?

Teams wiring QuantRisk into Claude, Cursor, or custom agents for finance.

Skip if: Skip when you need a non-MCP SDK or hosted API without stdio/SSE transport.

What you get

Working QuantRisk MCP server with verified tool registration and client config.

  • Agent-invokable VaR, Monte Carlo, optimization, Greeks, and stress-test results from QuantRisk
  • Repeatable quant checks documented in agent sessions for fintech build and grow loops

By the numbers

  • [object Object]
At a glance

QuantRisk capabilities & compatibility

Capabilities
quantrisk mcp tool registration · quantrisk client configuration · quantrisk agent workflow integration
Use cases
orchestration
Runs
Remote server

Tools 10

Public tool metadata - what this server can do for an agent.

analyze_risk6 params

Calculate core risk metrics for a portfolio — Value at Risk (VaR), Conditional VaR (CVaR), volatility, beta, and max drawdown.

  • positionsarrayrequiredArray of portfolio positions. Each entry needs a ticker and quantity. Free tier: max 20 positions. Paid tier: up to 500.
  • confidence_levelnumberVaR confidence level as a decimal, e.g. 0.95 = 95%. Range: 0.01-0.99. Default: 0.95.
  • horizon_daysintegerRisk horizon in trading days. 1 = overnight, 21 ≈ 1 month, 252 ≈ 1 year. Default: 1.
  • methodstringVaR calculation method. "historical" uses empirical return distribution, "parametric" assumes normality, "cornish_fisher" adjusts for skew and kurtosis. Default: "historical".
  • benchmarkstringBenchmark ticker for beta calculation, e.g. SPY or QQQ. Default: SPY.
  • lookback_daysintegerNumber of historical trading days to use. 252 ≈ 1 year, 756 ≈ 3 years. Range: 30-1260. Default: 252.
monte_carlo_simulation6 params

Run Monte Carlo simulation on a portfolio to model the distribution of future returns, including percentile outcomes and probability of loss.

  • positionsarrayrequiredArray of portfolio positions. Free tier: max 20 positions. Paid tier: up to 500.
  • num_pathsintegerNumber of simulation paths to run. More paths = more accurate but slower. Free tier: max 1,000. Paid tier: up to 100,000. Default: 10,000.
  • horizon_daysintegerSimulation horizon in trading days. 21 ≈ 1 month, 63 ≈ 1 quarter, 252 ≈ 1 year. Default: 21.
  • modelstringStochastic process model. "gbm" = Geometric Brownian Motion (standard), "jump_diffusion" = adds jump risk for fat-tail scenarios. Default: "gbm".
  • lookback_daysintegerHistorical window used to estimate drift and volatility parameters. Range: 30-1260 trading days. Default: 252.
  • seedRandom seed for reproducible results. Omit for a fresh random run each time.
stress_test3 params

Stress test a portfolio against historical crisis scenarios (GFC 2008, COVID 2020, etc.) or custom shocks (paid tier).

  • positionsarrayrequiredArray of portfolio positions. Free tier: max 20 positions and historical scenarios only. Paid tier: up to 500 positions plus custom shocks.
  • scenariosarrayHistorical scenarios to run. Available values: gfc_2008, covid_2020, dot_com_2000, black_monday_1987, taper_tantrum_2013, rate_hike_2022, volmageddon_2018, euro_crisis_2011. Default: [gfc_2008, covid_2020].
  • custom_shocksCustom shock definitions. PAID tier only. Each shock specifies ticker-level, sector-level, or market-wide price changes.
optimize_portfolio6 params

Find the optimal portfolio allocation using mean-variance optimization. Supports max Sharpe, min variance, and target return objectives. Paid tier only.

  • tickersarrayrequiredUniverse of tickers to optimize across. Must be 2-50 tickers. The optimizer will determine the best weights within this set.
  • objectivestringOptimization objective. "max_sharpe" = maximize risk-adjusted return, "min_variance" = minimize portfolio volatility, "target_return" = hit a specific return with minimum risk. Default: "max_sharpe".
  • target_returnRequired when objective is "target_return". Annualized return as a decimal, e.g. 0.12 = 12% annual return target.
  • constraintsobjectOptional weight constraints. See ConstraintsInput for details.
  • risk_free_ratenumberAnnualized risk-free rate as a decimal, e.g. 0.05 = 5%. Used in Sharpe ratio calculation. Default: 0.05.
  • lookback_daysintegerHistorical window for estimating return and covariance. 252 = 1 year, 756 = 3 years, 1260 = 5 years. Range: 252-1260. Default: 756.
correlation_matrix3 params

Compute the pairwise correlation matrix for a set of assets. Identifies highly correlated pairs and diversification opportunities.

  • tickersarrayrequiredTickers to include in the correlation matrix. Minimum 2, maximum 50. Free tier: max 10 tickers. Paid tier: up to 50.
  • lookback_daysintegerHistorical window for computing correlations in trading days. 30 = ~6 weeks, 252 = ~1 year. Range: 30-1260. Default: 252.
  • methodstringCorrelation method. "pearson" = linear correlation (standard), "spearman" = rank-based (robust to outliers), "kendall" = concordance-based. Default: "pearson".
performance_attribution4 params

Break down portfolio performance into factor exposures, sector allocation, and position contributions. Computes Sharpe, Sortino, Treynor, Calmar, and Information ratios.

  • positionsarrayrequiredArray of portfolio positions. Free tier: max 20 positions (basic ratios only). Paid tier: up to 500 positions with full factor attribution.
  • period_daysintegerMeasurement period in trading days. 252 = ~1 year. Range: 30-1260. Default: 252.
  • benchmarkstringBenchmark ticker for relative performance metrics (Information Ratio, Tracking Error, Beta). Default: SPY.
  • risk_free_ratenumberAnnualized risk-free rate as a decimal, e.g. 0.05 = 5%. Used in Sharpe, Sortino, and Treynor ratios. Default: 0.05.
sector_exposure1 param

Break down portfolio exposure by GICS sector, market cap, and asset class. Returns concentration metrics including the Herfindahl-Hirschman Index.

  • positionsarrayrequiredArray of portfolio positions to analyze. Returns GICS sector weights, market cap breakdown, and concentration metrics.
price_history3 params

Fetch historical OHLCV price data for one or more tickers. Free tier: 1 ticker, 252 days. Paid tier: up to 20 tickers, 1260 days.

  • tickersarrayrequiredTicker symbols to fetch price history for. Free tier: max 1 ticker. Paid tier: up to 20 tickers.
  • daysintegerNumber of historical trading days to return. Free tier: max 252 days (~1 year). Paid tier: up to 1260 days (~5 years). Default: 252.
  • intervalstringPrice interval. "daily" returns one OHLCV row per trading day, "weekly" aggregates to weekly bars, "monthly" aggregates to monthly bars. Default: "daily".
compare_portfolios3 params

Compare two or more portfolio allocations head-to-head across all key risk and return metrics. Paid tier only.

  • portfoliosarrayrequiredTwo to five named portfolios to compare head-to-head. Each needs a unique name and a list of positions. Min: 2, max: 5.
  • period_daysintegerLookback period in trading days used for return and risk calculations. 252 = ~1 year. Range: 30-1260. Default: 252.
  • confidence_levelnumberVaR confidence level as a decimal, e.g. 0.95 = 95%. Range: 0.01-0.99. Default: 0.95.
calculate_greeks2 params

Calculate option Greeks (delta, gamma, theta, vega, rho) for individual options or an options portfolio. Uses Black-Scholes for European, binomial for American style. Paid tier only.

  • optionsarrayrequiredArray of option positions to calculate Greeks for. 1-100 options. Results include per-option Greeks and aggregated portfolio Greeks.
  • risk_free_ratenumberAnnualized risk-free rate as a decimal, e.g. 0.05 = 5%. Used in Black-Scholes and binomial pricing models. Default: 0.05.
README.md

QuantRisk

Institutional-grade portfolio risk analytics for Claude and any MCP client.

npm version npm downloads License: MIT MCP Compatible

VaR / Monte Carlo / Stress Testing / Portfolio Optimization / Greeks / Correlation Matrices

Real market data. Real math. Not hallucinated numbers.

Website · Get Pro · Documentation

Quick Start

1. Install

npm install -g @quantrisk/mcp-server

2. Configure (Claude Desktop — see below for Cursor)

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "quantrisk": {
      "command": "quantrisk-mcp-server",
      "env": {
        "QUANTRISK_API_KEY": "your-api-key"
      }
    }
  }
}

Get your free API key at quantrisk.dev/signup.

3. Ask Claude

"What's the Value at Risk on a portfolio of 60% SPY, 25% TLT, and 15% GLD?"

That's it. Claude now has access to institutional-grade risk analytics.

Configuration

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "quantrisk": {
      "command": "quantrisk-mcp-server",
      "env": {
        "QUANTRISK_API_KEY": "your-api-key"
      }
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "quantrisk": {
      "command": "quantrisk-mcp-server",
      "env": {
        "QUANTRISK_API_KEY": "your-api-key"
      }
    }
  }
}

Any MCP Client

QuantRisk works with any client that supports the Model Context Protocol. Point it at the quantrisk-mcp-server binary with your API key in the environment.

Tools

Tool Description Tier
analyze_risk VaR, CVaR, volatility, Sharpe ratio, max drawdown Free
monte_carlo_simulation Forward-looking return simulations with configurable paths Free
stress_test Portfolio impact under historical and hypothetical scenarios Free
price_history Historical price and return data for any supported ticker Free
sector_exposure Sector and industry breakdown across holdings Free
performance_attribution Return attribution by asset, sector, and factor Free
correlation_matrix Cross-asset correlation analysis Free
optimize_portfolio Mean-variance and risk-parity optimization Pro
compare_portfolios Side-by-side risk/return comparison of multiple portfolios Pro
calculate_greeks Options Greeks — delta, gamma, theta, vega, rho Pro

Example Queries

Once configured, ask Claude questions like these:

  • "Run a Monte Carlo simulation on my portfolio: 50% AAPL, 30% MSFT, 20% NVDA. Show me the 5th percentile outcome."
  • "Stress test 70% VTI / 30% BND against the 2008 financial crisis and a hypothetical 300bp rate shock."
  • "What's my sector exposure if I hold equal weights in AMZN, JPM, JNJ, XOM, and NEE?"
  • "Show me the correlation matrix for SPY, GLD, TLT, and BTC-USD over the last 2 years."
  • "Compare the risk-adjusted returns of a 60/40 portfolio vs. an all-weather portfolio." (Pro)
  • "Calculate the Greeks for a SPY 550 call expiring in 30 days." (Pro)

Why Pro?

The free tier covers core risk analytics for small portfolios. Pro unlocks the tools and scale that serious analysis demands.

Free Pro ($29/mo)
Positions 20 500
API calls 50/day Unlimited
Tools 7 All 10
Monte Carlo paths 1,000 100,000
Portfolio optimization — Mean-variance, risk-parity, min-volatility
Portfolio comparison — Side-by-side multi-portfolio analysis
Options Greeks — Full Greeks surface

What that means in practice:

  • Free: "What's the VaR on my 10-stock portfolio?" — works great.
  • Pro: "Optimize my 200-position portfolio for maximum Sharpe, then stress test it against 5 scenarios and compare it to my current allocation." — you need Pro for that.

Upgrade to Pro

How It Works

Claude / MCP Client
      |
  MCP Protocol
      |
QuantRisk MCP Server (local process)
      |
QuantRisk API (Cloudflare Workers)
      |
Yahoo Finance (market data) + risk engine (math)
  • MCP Server runs locally as a stdio process — your API key never leaves your machine except to authenticate with the QuantRisk API.
  • Risk Engine runs on Cloudflare Workers. All calculations — VaR, Monte Carlo, optimization — happen server-side with real math on real market data.
  • Market Data sourced from Yahoo Finance. Prices, fundamentals, and options chains are fetched in real time.
  • Reports generated with pdf-lib when applicable.

No data is stored. No portfolio information is retained after a request completes.

Contributing

Contributions are welcome. Please open an issue first to discuss what you'd like to change.

git clone https://github.com/78degrees/mcp-server.git
cd mcp-server
npm install
npm test

See CONTRIBUTING.md for guidelines.

License

MIT

Built by the team at quantrisk.dev

Contact: hello@quantrisk.dev

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How it compares

Portfolio risk analytics MCP, not a generic data warehouse connector or tax filing skill.

FAQ

What does QuantRisk do?

Portfolio risk analytics - VaR, Monte Carlo, optimization, options Greeks, stress testing.

When should I use QuantRisk?

User asks about QuantRisk mcp, portfolio risk analytics - var, monte carlo, optimization, options gre.

Is this MCP server safe to install?

Review the Security Audits panel on this page before installing in production.

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