mcp-monte-carlo
Related Servers
Alternatives to mcp-monte-carlo
No user-submitted related servers found.
Related Servers
- AlicenseAqualityCmaintenanceProvides AI agents with quantitative risk tools such as VaR, expected shortfall, GARCH volatility, backtesting, stress testing, tail risk analysis, and credit scoring using synthetic or user-supplied data.71MIT
- FlicenseNot gradedqualityBmaintenanceEnables LLM agents to run iterative volatility analytics, from pre-flight statistical gates through GARCH-family model fitting, VaR/ES risk metrics, and Basel backtesting, with a feedback loop guiding each step.-
- FlicenseNot gradedqualityDmaintenanceEnables institutional-grade Monte Carlo risk analysis for portfolios, startups, real estate, and betting strategies using fat-tail distributions and proprietary algorithms. Provides comprehensive risk metrics including CVaR, VaR, ruin probability, and survival probability across multiple asset classes.1-
- AlicenseCqualityDmaintenanceProvides AI agents with institutional-grade quantitative finance tools including real-time market data, paper trading via Alpaca, risk analysis with Monte Carlo simulations, backtesting, and multi-source news sentiment analysis for portfolio management and trading strategy development.315MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to perform Black-Litterman portfolio optimization with investor views, backtesting, and asset analysis, generating dashboards for visualization.MIT
- FlicenseAqualityBmaintenanceProvides an LLM agent with extreme value statistics tools for tail risk analysis, including peaks-over-threshold GPD fitting, VaR and Expected Shortfall estimation, return levels, threshold-stability diagnostics, and out-of-sample VaR backtesting.6-
TDQS
Scored across 2 tools
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.
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.
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.
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.