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HPSILab - Quant Finance MCP Server for Stock Analysis and Options Analytics

by haiyunsky
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Python
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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

A4.6/5.0

Scored across 9 tools

Disambiguation5/5

Each tool targets a distinct aspect of stock/options analysis: aggregated analysis, chart generation, research reports, AI prediction, backtest curves, IV metrics, Monte Carlo simulation, options pressure, and pre-trade risk scan. Even the two comprehensive tools (analyze_stock and generate_stock_research_report) are clearly differentiated by output format (JSON vs. markdown) and recommended use cases.

Naming Consistency5/5

All 9 tools follow a consistent verb_noun pattern in snake_case: analyze_stock, generate_stock_images, generate_stock_research_report, get_ai_prediction, get_equity_curves, get_iv_radar, get_monte_carlo, get_option_pressure, get_pretrade_risk_scan. The verbs are appropriate and the naming is uniform.

Tool Count5/5

With 9 tools, the server is well-scoped for quantitative stock and options analytics. The tool count covers an aggregate analysis, specific data dimensions (AI, IV, Monte Carlo, options pressure, backtest, risk scan), and presentation (charts, reports) without unnecessary overlap or bloat.

Completeness5/5

The tool set provides comprehensive coverage for stock analysis and options analytics: directional prediction, volatility analysis, options positioning, Monte Carlo simulations, strategy backtests, risk scanning with portfolio context, and visualizations. There are no obvious gaps for the stated domain.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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