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haiyunsky

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

by haiyunsky
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_iv_radar

Read-onlyIdempotent

Retrieve implied-volatility metrics for one stock to judge whether options are cheap or expensive via IV rank/percentile, volatility regime, and skew direction.

Instructions

Retrieve implied-volatility (IV) metrics for a single stock.

Use this tool when:

  • You need to assess whether options are cheap or expensive relative to historical norms (IV rank / IV percentile).

  • You want the current volatility regime ("Low", "Normal", "Elevated", "Extreme") to frame risk sizing or strategy selection.

  • You are analyzing skew or risk-reversal direction (put-heavy vs call-heavy market).

Do NOT use this tool if you already called analyze_stock — the IV data is included in that response.

Returns

dict with keys: symbol : str — normalized ticker atm_iv : float — at-the-money implied volatility (annualized %) iv_rank : float — 0–100; ≥80 = expensive, ≤20 = cheap iv_percentile : float — historical percentile (0–100) risk_reversal : float — 25-delta risk reversal (positive = call-skew) volatility_regime: str — "Low" | "Normal" | "Elevated" | "Extreme"

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

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint, so the safety profile is covered. The description adds meaningful interpretation beyond that: IV rank thresholds ('≥80 = expensive, ≤20 = cheap'), regime categories, and the sign convention for risk reversal. It does not mention data staleness, source, or rate limits, so it stops short of full behavioral disclosure, but it is substantially richer than the annotations alone.

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?

The description is front-loaded with purpose, then use cases, then an exclusion, then returns—a logical structure. It is somewhat long because the 'Returns' section repeats keys that likely appear in the output schema, though the added interpretation thresholds justify most of that length.

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?

Given the single-parameter schema, the existing output schema, and the fully populated annotations, the description covers purpose, when/when-not to use it, and how to interpret the returned metrics. An agent has everything needed to call the tool correctly and understand its output semantics.

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?

Schema description coverage is 100% and the schema itself specifies uppercase ticker format, a regex pattern, and examples while explicitly forbidding company names. The description only adds 'for a single stock,' which clarifies that one symbol is accepted but is marginal against the fully documented schema. Baseline 3 is correct when the schema does the heavy lifting.

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 opens with a specific verb and resource: 'Retrieve implied-volatility (IV) metrics for a single stock.' It explicitly distinguishes itself from a sibling by warning 'Do NOT use this tool if you already called analyze_stock.' An agent can identify the tool's exact scope without opening the 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?

Three explicit 'Use this tool when' bullets cover cheap/expensive assessment, volatility regime framing, and skew analysis. The 'Do NOT use' clause names the alternative (analyze_stock) and the precise condition that selects it, leaving nothing to inference.

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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