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by dev.quantrisk
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monte_carlo_simulation

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

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "positions": "<positions>"
    },
    "name": "monte_carlo_simulation"
  }
}

Input Schema

NameRequiredDescriptionDefault
seedNoRandom seed for reproducible results. Omit for a fresh random run each time.
modelNoStochastic process model. "gbm" = Geometric Brownian Motion (standard), "jump_diffusion" = adds jump risk for fat-tail scenarios. Default: "gbm".gbm
num_pathsNoNumber 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.
positionsYesArray of portfolio positions. Free tier: max 20 positions. Paid tier: up to 500.
horizon_daysNoSimulation horizon in trading days. 21 ≈ 1 month, 63 ≈ 1 quarter, 252 ≈ 1 year. Default: 21.
lookback_daysNoHistorical window used to estimate drift and volatility parameters. Range: 30-1260 trading days. Default: 252.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool runs simulations and outputs distribution metrics, but does not mention key behaviors such as reliance on historical data (lookback_days), computational cost scaling with num_paths, or any side effects like data caching. The description is adequate but lacks depth on assumptions and limitations.

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

Conciseness4/5

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

The description is a single clear sentence that efficiently conveys the core functionality and outputs. It is front-loaded and uses precise language. While it could be broken into bullet points for readability, it is not unnecessarily verbose and earns its place.

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?

For a tool with 6 parameters and no output schema, the description provides a high-level summary of outputs ('percentile outcomes and probability of loss') but does not detail the structure of the results (e.g., return distribution, summary statistics). The schema covers parameters thoroughly, but the description could be more complete regarding expected output format and edge case handling.

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 parameters and their purposes. The description adds no additional parameter-level meaning beyond what is in the schema (e.g., does not explain how seed affects reproducibility or how model choice changes output). Baseline score of 3 is appropriate.

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

Purpose5/5

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

The description clearly states the tool's purpose: 'Run Monte Carlo simulation on a portfolio to model the distribution of future returns, including percentile outcomes and probability of loss.' This uses a specific verb ('Run') and resource ('Monte Carlo simulation on a portfolio'), and distinguishes it from sibling tools like stress_test or analyze_risk by specifying its unique output (distribution, percentile outcomes, probability of loss).

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

Usage Guidelines3/5

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

The description provides implied usage context (modeling future returns distribution) but does not explicitly state when to use this tool versus alternatives like 'analyze_risk' or 'stress_test'. No 'when not to use' or comparison to siblings is provided, leaving the agent to infer from the name and schema alone.

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

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