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shelendrajain2004

Financial Risk MCP Server

simulate_monte_carlo_pfe

Runs Monte Carlo simulation to project Potential Future Exposure (PFE) profiles and Peak Forward Exposure across multi-year tenors for a portfolio of trades.

Instructions

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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tradesYesList of trades to simulate exposure for
volatilityNoAnnualized market volatility factor
portfolio_idYesPortfolio identifier
mean_reversionNoMean reversion speed for rate/asset diffusion
num_simulationsNoNumber of Monte Carlo paths (default 10,000)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions an implementation detail (C++20 multithreaded core) but says nothing agent-relevant: no runtime/cost expectations, no determinism or seeding behavior, no statement about side effects or output format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no wasted framing, but the parenthetical about the native C++20 multithreaded core is implementation marketing that does not help an agent decide or invoke correctly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description must characterize the return, and it only names the outputs (PFE profiles, Peak Forward Exposure) without structure, granularity, or pagination/shape. For a 5-parameter computation tool with no annotations, this leaves real gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all five parameters and baseline is 3. The description adds one useful constraint not in the schema: the 0.25y–30y tenor range that bounds maturity_years, but nothing about notional, volatility, or simulation count semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Executes Monte Carlo simulation) and resource (PFE profiles / Peak Forward Exposure) with the tenor range made explicit. It is clear what the tool computes, though it never distinguishes itself from siblings like calculate_sacr_exposure or compute_portfolio_var.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this tool versus the sibling exposure/VaR/Greeks tools, nor any prerequisites or exclusions. Usage must be inferred purely from the tool name and purpose sentence.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.