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Independent project, not affiliated with Anthropic
78degrees avatar

QuantRisk

78degrees/mcp-server
10 toolsauthSTDIO, HTTPregistry active
Summary

Plugs institutional risk analytics directly into Claude via the Model Context Protocol. Exposes 10 tools including VaR calculation, Monte Carlo simulation with configurable paths, historical stress testing, portfolio optimization, and options Greeks. Free tier gets you 7 tools and 20 positions per portfolio. Pro unlocks mean-variance optimization, 500-position portfolios, and 100k simulation paths. Runs as a local stdio process that talks to a Cloudflare Workers backend, which pulls real market data from Yahoo Finance and runs the math server-side. Useful when you need actual calculated risk metrics instead of LLM estimates, or when you're building portfolio analysis workflows that need to ground decisions in real numbers.

Install to Claude Code

verified
claude mcp add --transport http mcp-server https://quantrisk-mcp.quantrisk.workers.dev/mcp

Run in your terminal. Add --scope user to make it available in every project.

Review the command, arguments, and environment values before installing — MCP servers run with your local permissions.

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Tools

Verified live against the running server on Jun 10, 2026.

verified live10 tools
analyze_riskCalculate core risk metrics for a portfolio — Value at Risk (VaR), Conditional VaR (CVaR), volatility, beta, and max drawdown.6 params

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

Parameters* required
benchmarkstring
Benchmark ticker for beta calculation, e.g. SPY or QQQ. Default: SPY.default: SPY
confidence_levelnumber
VaR confidence level as a decimal, e.g. 0.95 = 95%. Range: 0.01-0.99. Default: 0.95.default: 0.95
horizon_daysinteger
Risk horizon in trading days. 1 = overnight, 21 ≈ 1 month, 252 ≈ 1 year. Default: 1.default: 1
lookback_daysinteger
Number of historical trading days to use. 252 ≈ 1 year, 756 ≈ 3 years. Range: 30-1260. Default: 252.default: 252
methodstring
VaR calculation method. "historical" uses empirical return distribution, "parametric" assumes normality, "cornish_fisher" adjusts for skew and kurtosis. Default: "historical".one of historical · parametric · cornish_fisherdefault: historical
positions*array
Array of portfolio positions. Each entry needs a ticker and quantity. Free tier: max 20 positions. Paid tier: up to 500.
monte_carlo_simulationRun Monte Carlo simulation on a portfolio to model the distribution of future returns, including percentile outcomes and probability of loss.6 params

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

Parameters* required
horizon_daysinteger
Simulation horizon in trading days. 21 ≈ 1 month, 63 ≈ 1 quarter, 252 ≈ 1 year. Default: 21.default: 21
lookback_daysinteger
Historical window used to estimate drift and volatility parameters. Range: 30-1260 trading days. Default: 252.default: 252
modelstring
Stochastic process model. "gbm" = Geometric Brownian Motion (standard), "jump_diffusion" = adds jump risk for fat-tail scenarios. Default: "gbm".one of gbm · jump_diffusiondefault: gbm
num_pathsinteger
Number 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.default: 10000
positions*array
Array of portfolio positions. Free tier: max 20 positions. Paid tier: up to 500.
seedvalue
Random seed for reproducible results. Omit for a fresh random run each time.
stress_testStress test a portfolio against historical crisis scenarios (GFC 2008, COVID 2020, etc.) or custom shocks (paid tier).3 params

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

Parameters* required
custom_shocksvalue
Custom shock definitions. PAID tier only. Each shock specifies ticker-level, sector-level, or market-wide price changes.
positions*array
Array of portfolio positions. Free tier: max 20 positions and historical scenarios only. Paid tier: up to 500 positions plus custom shocks.
scenariosarray
Historical 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, cov...
optimize_portfolioFind the optimal portfolio allocation using mean-variance optimization. Supports max Sharpe, min variance, and target return objectives. Paid tier only.6 params

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

Parameters* required
constraintsobject
Optional weight constraints. See ConstraintsInput for details.
lookback_daysinteger
Historical window for estimating return and covariance. 252 = 1 year, 756 = 3 years, 1260 = 5 years. Range: 252-1260. Default: 756.default: 756
objectivestring
Optimization objective. "max_sharpe" = maximize risk-adjusted return, "min_variance" = minimize portfolio volatility, "target_return" = hit a specific return with minimum risk. Default: "max_sharpe".one of max_sharpe · min_variance · target_returndefault: max_sharpe
risk_free_ratenumber
Annualized risk-free rate as a decimal, e.g. 0.05 = 5%. Used in Sharpe ratio calculation. Default: 0.05.default: 0.05
target_returnvalue
Required when objective is "target_return". Annualized return as a decimal, e.g. 0.12 = 12% annual return target.
tickers*array
Universe of tickers to optimize across. Must be 2-50 tickers. The optimizer will determine the best weights within this set.
correlation_matrixCompute the pairwise correlation matrix for a set of assets. Identifies highly correlated pairs and diversification opportunities.3 params

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

Parameters* required
lookback_daysinteger
Historical window for computing correlations in trading days. 30 = ~6 weeks, 252 = ~1 year. Range: 30-1260. Default: 252.default: 252
methodstring
Correlation method. "pearson" = linear correlation (standard), "spearman" = rank-based (robust to outliers), "kendall" = concordance-based. Default: "pearson".one of pearson · spearman · kendalldefault: pearson
tickers*array
Tickers to include in the correlation matrix. Minimum 2, maximum 50. Free tier: max 10 tickers. Paid tier: up to 50.
performance_attributionBreak down portfolio performance into factor exposures, sector allocation, and position contributions. Computes Sharpe, Sortino, Treynor, Calmar, and Information ratios.4 params

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

Parameters* required
benchmarkstring
Benchmark ticker for relative performance metrics (Information Ratio, Tracking Error, Beta). Default: SPY.default: SPY
period_daysinteger
Measurement period in trading days. 252 = ~1 year. Range: 30-1260. Default: 252.default: 252
positions*array
Array of portfolio positions. Free tier: max 20 positions (basic ratios only). Paid tier: up to 500 positions with full factor attribution.
risk_free_ratenumber
Annualized risk-free rate as a decimal, e.g. 0.05 = 5%. Used in Sharpe, Sortino, and Treynor ratios. Default: 0.05.default: 0.05
sector_exposureBreak down portfolio exposure by GICS sector, market cap, and asset class. Returns concentration metrics including the Herfindahl-Hirschman Index.1 params

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

Parameters* required
positions*array
Array of portfolio positions to analyze. Returns GICS sector weights, market cap breakdown, and concentration metrics.
price_historyFetch historical OHLCV price data for one or more tickers. Free tier: 1 ticker, 252 days. Paid tier: up to 20 tickers, 1260 days.3 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.

Parameters* required
daysinteger
Number of historical trading days to return. Free tier: max 252 days (~1 year). Paid tier: up to 1260 days (~5 years). Default: 252.default: 252
intervalstring
Price interval. "daily" returns one OHLCV row per trading day, "weekly" aggregates to weekly bars, "monthly" aggregates to monthly bars. Default: "daily".one of daily · weekly · monthlydefault: daily
tickers*array
Ticker symbols to fetch price history for. Free tier: max 1 ticker. Paid tier: up to 20 tickers.
compare_portfoliosCompare two or more portfolio allocations head-to-head across all key risk and return metrics. Paid tier only.3 params

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

Parameters* required
confidence_levelnumber
VaR confidence level as a decimal, e.g. 0.95 = 95%. Range: 0.01-0.99. Default: 0.95.default: 0.95
period_daysinteger
Lookback period in trading days used for return and risk calculations. 252 = ~1 year. Range: 30-1260. Default: 252.default: 252
portfolios*array
Two to five named portfolios to compare head-to-head. Each needs a unique name and a list of positions. Min: 2, max: 5.
calculate_greeksCalculate 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.2 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.

Parameters* required
options*array
Array of option positions to calculate Greeks for. 1-100 options. Results include per-option Greeks and aggregated portfolio Greeks.
risk_free_ratenumber
Annualized risk-free rate as a decimal, e.g. 0.05 = 5%. Used in Black-Scholes and binomial pricing models. Default: 0.05.default: 0.05

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

ToolDescriptionTier
analyze_riskVaR, CVaR, volatility, Sharpe ratio, max drawdownFree
monte_carlo_simulationForward-looking return simulations with configurable pathsFree
stress_testPortfolio impact under historical and hypothetical scenariosFree
price_historyHistorical price and return data for any supported tickerFree
sector_exposureSector and industry breakdown across holdingsFree
performance_attributionReturn attribution by asset, sector, and factorFree
correlation_matrixCross-asset correlation analysisFree
optimize_portfolioMean-variance and risk-parity optimizationPro
compare_portfoliosSide-by-side risk/return comparison of multiple portfoliosPro
calculate_greeksOptions Greeks — delta, gamma, theta, vega, rhoPro

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.

FreePro ($29/mo)
Positions20500
API calls50/dayUnlimited
Tools7All 10
Monte Carlo paths1,000100,000
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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Configuration

QUANTRISK_API_KEYsecret

API key from https://quantrisk.dev/upgrade. Optional — without one, requests run on the free tier (limited tools and call quotas).

Categories
Monitoring & Observability
Registryactive
Package@quantrisk/mcp-server
TransportSTDIO, HTTP
AuthRequired
Tools verifiedJun 10, 2026
UpdatedMay 9, 2026
View on GitHub

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