Financial Risk MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PYTHONPATH | Yes | Python path environment variable set to 'src' so the fin_risk_mcp module can be found when running the server. |
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
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| calculate_sacr_exposureB | Calculates Basel III / BCBS 279 Standardized Approach for Counterparty Credit Risk (SA-CCR) metrics: Replacement Cost (RC), Potential Future Exposure (PFE), Supervisory Add-on, Multiplier, and Exposure at Default (EAD = 1.4 * (RC + PFE)). |
| simulate_monte_carlo_pfeC | Executes high-performance Monte Carlo simulation (using native C++20 multithreaded core) to project Potential Future Exposure (PFE) profiles and Peak Forward Exposure across multi-year tenors (0.25y to 30y). |
| compute_portfolio_varA | Computes regulatory Value-at-Risk (VaR) and Expected Shortfall (CVaR) for market risk across specified holding periods and confidence intervals. |
| calculate_portfolio_greeksC | Aggregates first- and second-order derivatives sensitivities: Delta, Gamma, Vega, Theta, and Rho across a book of positions. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| audit_counterparty_risk | Prompts the LLM agent to conduct an audit of counterparty credit risk and SA-CCR capital adequacy. |
| stress_test_scenario | Prompts the LLM agent to evaluate portfolio vulnerability under rate shocks and volatility spikes. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Benchmark Multi-Asset Derivative Portfolio | Standard institutional portfolio containing Rates, FX, and Equity options for risk benchmarking |
| BCBS 279 Supervisory Risk Factors Table | Basel III supervisory factors, duration formulas, and correlations across asset classes |
TDQS
Scored across 4 tools
Each tool targets a distinct risk metric: regulatory SA-CCR exposure, Monte Carlo PFE profiles, portfolio VaR/CVaR, and Greeks. The only mild overlap is between calculate_sacr_exposure and simulate_monte_carlo_pfe, since both address counterparty exposure, but the standardized-formula vs simulation distinction is clear enough to distinguish them.
All names use snake_case with a verb_noun structure (calculate_sacr_exposure, simulate_monte_carlo_pfe, compute_portfolio_var, calculate_portfolio_greeks), which is easily predictable. The verb choices vary (calculate/simulate/compute) but all are equally readable and follow the same pattern.
Four tools is a focused, well-scoped set that avoids redundancy for a risk analytics server. It is on the lean side, but each tool clearly earns its place.
The core measures are covered: exposure, PFE, VaR/CVaR, and Greeks. However, notable counterparty-risk operations are absent, including CVA/credit valuation adjustment, stress testing, and scenario or backtesting tools, leaving some obvious gaps in a full risk workflow.