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

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

by haiyunsky
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

analyze_stock

Read-onlyIdempotent

Aggregate AI prediction, implied volatility, options pressure, Monte Carlo, and backtest signals into a unified bull/bear verdict for a single stock.

Instructions

Run a full institutional-grade quantitative analysis for a single stock.

This is the primary tool for a complete market view. It aggregates results from AI prediction, implied-volatility radar, options-pressure map, Monte Carlo simulation, and strategy backtesting into one unified signal.

Use this tool when:

  • You need a holistic bull/bear verdict with supporting evidence.

  • You want to compare multiple signal sources in a single call.

  • A user asks for a "stock analysis", "market view", or "trading signal".

Prefer the dedicated sub-tools (get_iv_radar, get_monte_carlo, etc.) when you need only a specific data dimension, to reduce latency and token usage.

Returns

dict with keys: symbol : str — normalized ticker signal : str — "Bullish" | "Bearish" | "Neutral" confidence_score: int — 0–100 directional confidence bullish_factors : list — evidence supporting an upward move bearish_factors : list — evidence supporting a downward move summary : str — one-sentence synthesis

Notes

  • Requires a valid HPSILAB_API_KEY.

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

  • Response latency is ~5–15 s due to multi-model aggregation.

Input Schema

NameRequiredDescriptionDefault
symbolYesExchange ticker in uppercase, e.g. 'NVDA', 'AAPL', 'SPY', 'QQQ'. Do NOT pass company names ('Nvidia') — use official tickers only.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.11.1
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "analyze_stockDictOutput",
+  "type": "object"
+}
  2. Changed3 schema fields changedv0.3.1
    • addedInput schema / properties / symbol / description
      Added value: +"Exchange ticker in uppercase, e.g. 'NVDA', 'AAPL', 'SPY', 'QQQ'. Do NOT pass company names ('Nvidia') — use official tickers only."
    • addedInput schema / properties / symbol / examples
      Added value: +[
+  "NVDA",
+  "AAPL",
+  "SPY",
+  "QQQ"
+]
    • addedInput schema / properties / symbol / pattern
      Added value: +"^[A-Z][A-Z0-9.-]{0,15}$"
  3. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing the HPSILAB_API_KEY requirement, account-governed quota and ticker coverage, and a 5–15 s latency from multi-model aggregation — all decision-relevant for an agent planning a call. Annotations already declare readOnly/idempotent/non-destructive, and nothing here contradicts them.

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?

Front-loads the one-line purpose, then when-to-use, then the alternative — a good order with no filler prose. The full 'Returns' key listing duplicates the existing output schema and is the one block that does not earn its space.

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

Completeness5/5

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

For a single-symbol aggregator, everything an agent needs is present: what it computes, when to call it, what it costs in latency/quota, and confirmation of the auth prerequisite. Return values are independently covered by the output schema, so the duplicated listing is excess rather than a gap.

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?

With a single parameter at 100% schema description coverage, the schema already carries the uppercase-ticker format, pattern and examples. The description adds no parameter-level guidance of its own (e.g. market/exchange limitations), so the baseline 3 for schema-driven parameters applies.

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?

States a specific verb and resource ('Run a full institutional-grade quantitative analysis for a single stock') and explicitly positions itself as the aggregator of AI prediction, IV radar, options pressure, Monte Carlo and backtesting. It names the dedicated sub-tools it supersedes, so an agent can distinguish it from get_iv_radar, get_monte_carlo, etc. without opening any schema.

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?

Gives an explicit 'Use this tool when' list tied to user phrasings ('stock analysis', 'market view', 'trading signal') plus the inverse rule: prefer the dedicated sub-tools when only one data dimension is needed, to reduce latency and token usage. Both the when and the when-not are stated with named alternatives.

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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