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chenxi-bot21

risk-analytics-mcp-server

by chenxi-bot21
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
compute_var_esA

Portfolio VaR and Expected Shortfall by four methods (historical, parametric-normal, Cornish-Fisher, Monte Carlo) at confidence alpha.

asset_returns maps asset name -> equal-length list of daily returns; weights defaults to equal. Omit both to use the demo book. Divergence between historical and normal VaR is the fat-tail signal.

garch_volatilityA

Fit GARCH(1,1) by maximum likelihood to a daily return series (>= 250 obs) and forecast volatility forecast_horizon days ahead (mean-reverting to long-run vol). Omit returns to use the demo portfolio.

backtest_varA

Walk-forward VaR backtest (no look-ahead): Kupiec proportion-of-failures, Christoffersen independence/conditional-coverage tests, and the Basel traffic-light zone. method is 'historical' or 'parametric'. Needs more than window+30 observations; omit returns to use the demo portfolio.

stress_testC

Stress the portfolio two ways: a preset crisis-shock library (GFC-style equity crash, 2020 pandemic, +200bp rates, flight to quality, USD squeeze) and the portfolio's own worst horizon-day historical windows.

evt_tail_riskA

Extreme-value tail analysis: fit a Generalized Pareto to losses beyond the threshold_q quantile (peaks-over-threshold) and report EVT VaR/ES at alpha (use for 99.5%+ where empirical quantiles run out of data).

score_credit_applicationA

Score a retail loan application on a WoE logistic PD scorecard: 12-month probability of default, scorecard points (higher = safer) and a letter rating. Demo model trained on a synthetic book — methodology is production-style, the score is not a production score.

credit_model_summaryA

Metadata for the credit scorecard: held-out AUROC/Gini/KS, per-feature Information Values, and the valid categorical inputs.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored across 7 tools

Disambiguation5/5

Each tool targets a distinct area of risk analytics: VaR computation, backtesting, credit scoring, EVT, GARCH, stress testing, and credit model metadata. No two tools overlap in purpose.

Naming Consistency4/5

All names use lowercase with underscores, but some follow a verb_noun pattern (e.g., backtest_var, compute_var_es) while others are noun phrases (e.g., credit_model_summary, garch_volatility). This minor inconsistency does not hinder readability.

Tool Count5/5

Seven tools cover the core risk analytics workflow without being excessive. Each tool earns its place, covering computation, backtesting, credit risk, and stress testing.

Completeness4/5

The set covers key risk functions: VaR/ES estimation, backtesting, volatility modeling, tail risk, credit scoring, and stress testing. Missing features like data ingestion or portfolio optimization are minor gaps.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources