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

generate_stock_research_report

Generate a structured market research report for a stock, combining AI predictions, volatility analysis, options positioning, Monte Carlo outlook, and strategy backtests into a single markdown document.

Instructions

Generate a structured, institutional-style markdown research report for a single stock, covering all major quantitative signal sources.

The report is divided into six sections:

  1. Executive Summary — bull/bear verdict, confidence score, one-line thesis

  2. AI Prediction — ensemble model votes, up-probability, regime

  3. Volatility Analysis — ATM IV, IV rank, vol regime, risk reversal

  4. Options Positioning — max pain, gamma wall, expected move, squeeze targets

  5. Monte Carlo Outlook — 30-day price distribution, 90 %/68 % confidence ranges

  6. Strategy Backtests — Sharpe, max drawdown, win rate across quant strategies

Output is a complete markdown string (~800–1200 words) ready to render or share. Response latency is ~10–20 s due to full multi-model data aggregation.

Use this tool when:

  • A user asks for a "report", "write-up", "research note", or "deep dive".

  • You want a pre-formatted narrative combining all signal sources in one document.

  • You need output suitable for archiving, PDF export, or investor communication.

Do NOT use this tool when:

  • You only need a quick directional verdict → use analyze_stock instead.

  • You need a specific data dimension (IV, Monte Carlo, etc.) → use the dedicated sub-tool (get_iv_radar, get_monte_carlo, etc.) for lower latency.

Parameters

symbol : str Exchange ticker in uppercase, e.g. "NVDA", "TSLA", "SPY". Do NOT pass company names — use official tickers only.

Returns

dict with keys: symbol : str — normalized ticker report : str — full markdown report (~800–1200 words, 6 sections) generated_at : str — ISO 8601 generation timestamp

Notes

  • Requires a valid HPSILAB_API_KEY.

  • API access, quota, and ticker coverage are governed by the HPSILab account.

  • For programmatic use, prefer analyze_stock which returns structured JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesExchange ticker in uppercase, e.g. 'NVDA', 'TSLA', 'SPY'. Do NOT pass company names — use official tickers only.
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses latency (~10-20 s), reliance on HPSILAB_API_KEY, and that output is a full markdown report of ~800-1200 words. Could mention if it's read-only or modifies state, but reasonable given context.

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?

Well-structured with headings, bullet points, and clear sections. Front-loaded with purpose and six sections list. Slightly verbose but each sentence adds value. Could tighten 'Output is a complete markdown string' vs earlier mention, but overall effective.

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

Completeness4/5

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

Given no output schema, description provides return keys (symbol, report, generated_at) and report length. Lists six sections in detail. Also includes notes on API key and programmatic alternatives. Covers most information an agent needs to decide and use the tool.

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

Parameters4/5

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

Only one parameter symbol with 100% schema description coverage. Description adds meaningful guidance beyond schema: 'Do NOT pass company names — use official tickers only.' This helps avoid common errors.

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 it generates a structured markdown research report for a single stock covering all major quantitative signal sources. It distinguishes itself from siblings like analyze_stock (structured JSON), get_iv_radar, get_monte_carlo, etc., by noting these are faster for specific data.

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

Usage Guidelines5/5

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

Explicitly states when to use (user asks for report, write-up, deep dive) and when not to use (quick directional verdict → analyze_stock, specific data dimension → sub-tools). Also mentions latency of 10-20 seconds as a trade-off.

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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