Risk Analytics Server

An MCP server that gives AI agents quantitative risk tools.

Any MCP client (Claude Code, Claude Desktop, or your own agent) gets seven tools backed by two real engines — market-risk-engine and credit-risk-model:

Tool	What it does
compute_var_es	Portfolio VaR & Expected Shortfall four ways — historical, parametric-normal, Cornish-Fisher, Monte Carlo — so the agent can compare methods, not just get a number.
garch_volatility	GARCH(1,1) fit by maximum likelihood (no arch dependency) + mean-reverting h-day vol forecast.
backtest_var	Walk-forward VaR backtest with Kupiec POF, Christoffersen independence / conditional-coverage tests, and the Basel traffic-light zone.
stress_test	Preset crisis-shock library (GFC equity crash, 2020 pandemic, +200bp rates, flight to quality, USD squeeze) + the portfolio's own worst historical windows.
evt_tail_risk	Peaks-over-threshold GPD tail fit; EVT VaR/ES for the 99.5%+ region where empirical quantiles run out of data.
score_credit_application	12-month PD, scorecard points and letter rating from a WoE logistic scorecard (PDO points scaling).
credit_model_summary	The scorecard's held-out AUROC/Gini/KS and per-feature Information Values.

Every market tool works with no data at all — omit the returns and it runs on a seeded 4-asset synthetic demo book (EQUITY/BOND/GOLD/FX, ~5 trading years), so an agent can explore the tools fully offline. Pass your own daily returns (fractions, 0.01 = 1%) to analyze a real portfolio. VaR/ES are reported as positive daily loss fractions.

The credit scorecard is trained once per process on the engine's seeded synthetic 12k-loan book and cached; the methodology (monotonic WoE binning, logistic regression, points scaling, ratings) is the production pattern, the score itself is a demo.

Install & connect

pip install git+https://github.com/chenxi-bot21/risk-analytics-mcp-server.git

Claude Code:

claude mcp add risk -- risk-mcp

Claude Desktop / any MCP client (stdio transport):

{
  "mcpServers": {
    "risk": { "command": "risk-mcp" }
  }
}

Or without installing, via uv:

{
  "mcpServers": {
    "risk": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/chenxi-bot21/risk-analytics-mcp-server.git", "risk-mcp"]
    }
  }
}

Example prompts once connected

• "What's the 99% VaR of a portfolio that's 60% equity, 30% bonds, 10% gold? Compare methods — do the tails look fat?"
• "Backtest a 99% historical VaR on these returns and tell me which Basel zone it lands in." (paste returns)
• "Score this applicant: 24 years old, $25k income, $30k loan at 26%, DTI 42, utilization 130%, 4 delinquencies…"

Architecture

src/risk_mcp/
├── market.py   # JSON-friendly wrappers around marketrisk (pure functions)
├── credit.py   # cached synthetic-trained WoE scorecard + scoring
└── server.py   # FastMCP registration shim — no logic of its own

The wrappers are plain functions with plain-type signatures, so the whole surface is unit-tested (19 tests) without a running server; one test drives a tool through the actual MCP protocol layer.

python -m unittest discover -s tests -t .

License

MIT.