hpsilab-mcp

Implied Volatility Radar

get_iv_radar

Read-onlyIdempotent

Implied-volatility (IV) structure for a stock: how expensive options are, whether volatility is being squeezed, and whether traders are paying up for upside (calls) or downside (puts). Available to all signed-in users.

Args:
    ticker: Stock symbol, e.g. "NVDA".
    refresh: Bypass the backend's fresh IV cache and request the latest
        option-chain pull. Defaults to False.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ticker`	Yes
`refresh`	No

TDQS

A4.2/5.0

Behavior4/5

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

Beyond the annotations (read-only, idempotent, non-destructive), the description adds useful behavioral context: the refresh parameter reveals a caching mechanism that can be bypassed for the latest option-chain pull, and it explains what the IV structure indicates. This adds meaningful context not inferable from 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a single purpose sentence followed by an Args section. Every sentence adds value, front-loading the core function and then clarifying parameters without any redundant or filler content.

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?

For a simple get-only tool with strong annotations and only two parameters, the description covers the key context: what the IV radar measures and how to request fresh data. Though there is no output schema, the description conveys the type of insight returned; a minor gap is that it doesn't specify the exact return shape, but it remains sufficiently informative.

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?

The schema provides only names/types, but the description enriches both parameters: ticker is illustrated with an example, and refresh is explained as a cache-bypass flag. Since schema description coverage is 0%, the description compensates well by clarifying the functional role of each parameter.

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 the tool's function: providing implied-volatility structure for a stock, detailing what it measures (option expense, volatility squeeze, call/put skew). It uses a specific verb+resource pattern ('IV structure for a stock') and distinguishes itself from siblings like get_option_pressure by focusing on IV, not volume/pressure.

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

Usage Guidelines3/5

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

The description shows the tool is available to all signed-in users and implies usage for IV analysis, but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. No sibling comparison is provided, so the guidance is inferred rather than explicit.

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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TDQS

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct purpose: individual quant analyses (AI prediction, IV, Monte Carlo, option pressure, equity curve, risk scan), aggregation (analyze_stock), output generation (images, report), and account registration. No two tools are likely to be confused.

Naming Consistency5/5

All tool names follow a uniform verb_noun snake_case pattern (e.g., get_ai_prediction, generate_stock_images, register_account). The convention is applied consistently across the entire set.

Tool Count5/5

10 tools is well within the ideal 3-15 range and covers the full stock-analysis workflow: data gathering, analysis, aggregation, and report generation. Each tool contributes distinct functionality without bloat.

Completeness5/5

The tool set comprehensively covers the domain of quant stock analysis: predictive models, backtesting, options/IV analysis, risk scanning, aggregated analysis, and visual/report outputs. No obvious dead ends or missing core operations for the intended purpose.

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