Skip to main content
Glama
alexmartinsgomes

mcp-monte-carlo

by alexmartinsgomes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

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
forecast_asset_monte_carloA

Run a forward Monte Carlo forecast of an asset's future price distribution.

Use this when the user wants scenario ranges, risk, or path statistics for a
ticker (e.g. SPY, AAPL): price/return percentiles, annualized volatility,
max-drawdown percentiles, and loss/gain probabilities at 7d, 30d, 3m, 6m,
1y, 3y, 5y, and 10y trading-day horizons.

Downloads max adjusted daily closes, fits EGARCH(1,1) with leverage
(o=1) and skewed-t innovations (historical mean drift), then simulates
``n_paths`` paths.

Prefer ``inspect_asset_model`` first only when you need fit/data diagnostics
without simulating paths.

Args:
    ticker: Yahoo Finance ticker symbol (e.g. SPY, AAPL).
    n_paths: Number of Monte Carlo paths (default 5000, minimum 100).
inspect_asset_modelA

Inspect the EGARCH + skewed-t model fit for a ticker WITHOUT simulating paths.

Call this when you need to validate data quality or model sanity before (or
instead of) a full Monte Carlo forecast — for example: Is there enough
history? What is today's conditional volatility? Do residuals look heavily
skewed/fat-tailed? What are the fitted EGARCH and skew-t parameters?

Do NOT use this for forward price scenarios, percentiles, drawdowns, or
probabilities — use ``forecast_asset_monte_carlo`` for those.

Returns JSON with history span, last price, fitted parameters, AIC/BIC,
last conditional volatility (daily and annualized), and residual
skewness/excess kurtosis.

Args:
    ticker: Yahoo Finance ticker symbol (e.g. SPY, AAPL).

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

A4.7/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have fully distinct purposes: one generates forward Monte Carlo forecasts, while the other inspects model fit without simulating. Each description explicitly states when to use it and when not to, so there is no realistic ambiguity.

Naming Consistency5/5

Both tool names follow a consistent verb-first snake_case pattern: forecast_asset... and inspect_asset.... The shared '_asset_' segment reinforces that they operate on the same domain, and there is no mix of naming conventions.

Tool Count3/5

Two tools is on the low edge of what feels like a reasonable server surface. Each tool serves a necessary role in the workflow, but the server is minimal and could feel thin to agents expecting additional financial utilities.

Completeness4/5

For the stated purpose, the core workflow is covered: fit/inspect the model and run forecasts. There is no obvious dead end for the main Monte Carlo use cases, though a direct historical data or backtesting tool would make the surface more complete.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources