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Alphanume Datasets

by com.alphanume

IV vs HV Premium Screener

get_iv_hv_premium
Read-onlyIdempotent

Volatility risk premium screener: answers "are this name's options rich or cheap right now?" For every liquid US optionable equity, per trading day: ~30-day at-the-money implied volatility (iv) vs ~30-day realized volatility (hv), their spread (iv-hv) and ratio (iv/hv), plus daily cross-sectional percentile ranks and z-scores for each measure, option notional volume, and the ATM strike/expiry/spot used. Rows update intraday (is_final=0) and settle after the close (is_final=1).

Use it to find overpriced premium to sell (high iv_hv_ratio / min_ratio_rank near 1), underpriced options to buy, or to track one ticker's premium history via ticker.

Requires an Alphanume Pro API key. A 403 PRO_SUBSCRIPTION_REQUIRED or DATE_RANGE_RESTRICTED error means the key's plan does not cover the request -- it does not mean the data is missing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoExact date, YYYY-MM-DD. Cannot be combined with the date range parameters.
tickerNoTicker symbol filter, e.g. 'AAPL'. Case-insensitive.
date_gtNoStart of date range, exclusive (YYYY-MM-DD).
date_ltNoEnd of date range, exclusive (YYYY-MM-DD).
date_gteNoStart of date range, inclusive (YYYY-MM-DD).
date_lteNoEnd of date range, inclusive (YYYY-MM-DD).
max_rowsNoMaximum data rows to return to the client (applied after the API responds). Default 500. Use 0 for no cap. Prefer narrowing with date/ticker filters over raising this.
only_finalNoIf true, return only settled end-of-day rows (is_final=1). By default the latest value is returned, which intraday may be provisional.
min_ratio_rankNoOnly rows whose iv/hv ratio sits at or above this cross-sectional percentile for the day, in [0, 1]. 0.95 = the day's richest 5%.
max_iv_hv_ratioNoOnly rows with iv_hv_ratio <= this value (e.g. 0.9 to screen for cheap options).
min_iv_hv_ratioNoOnly rows with iv_hv_ratio >= this value (e.g. 1.5 for names whose options price 50%+ over realized vol).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses meaningful runtime behavior: rows update intraday with is_final=0 and settle after close with is_final=1. It also explains the API-key requirement and clarifies that 403 errors mean plan restrictions, not missing data. This is exactly the kind of context that helps an agent interpret unexpected responses.

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 dense but every sentence earns its place: the screener's question, the universe and metrics, intraday/settlement behavior, use cases, filter hints, and auth/error semantics. It is front-loaded with the core purpose and avoids filler or repetition of schema details.

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 an 11-parameter tool with no output schema, the description gives the agent enough context to call it correctly and interpret what comes back. It enumerates return row contents, explains is_final semantics, provides screening examples, and covers the one likely error condition. Nothing essential is missing.

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 input schema already covers 100% of parameters, so the baseline is 3. The description adds interpretive value by linking filters to real screening goals, such as using min_iv_hv_ratio >= 1.5 for options priced 50% over realized vol and max_iv_hv_ratio for cheap options. This contextual framing goes beyond the schema's literal parameter definitions.

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 question ('are this name's options rich or cheap right now?') and enumerates the exact metrics computed: 30-day ATM IV vs HV, spread, ratio, percentile ranks, z-scores, notional volume, and ATM details. This clearly differentiates it from sibling tools like get_iv_rank or get_vol_of_vol by focusing on the IV/HV premium cross-sectional screener rather than single-name rank or volatility-of-volatility.

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

Usage Guidelines4/5

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

The description explicitly states when to use the tool: to find overpriced premium to sell, underpriced options to buy, or to track a ticker's premium history via `ticker`. It also gives concrete filter-based guidance like high iv_hv_ratio / min_ratio_rank near 1. However, it does not explicitly say when not to use it or name an alternative sibling for adjacent use cases.

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

A4.2/5.0
Disambiguation4/5

Each tool maps to a distinct dataset, and the descriptions are detailed enough to resolve most ambiguity. A few adjacent pairs (S-1 dilution vs. shelf registrations, IV-HV premium vs. IV rank, FDA votes vs. FDA adverse events) share thematic surface area and could be confused by name alone.

Naming Consistency4/5

The overwhelming majority of tools follow a clean get_<noun_phrase> snake_case pattern. The two exceptions, check_api_status and list_market_cap_tickers, are semantically appropriate utility/companion tools but break the otherwise uniform verb prefix.

Tool Count3/5

At 27 tools, the surface is heavy and spans many unrelated financial domains, making selection and prompt context more expensive. Each tool does earn its place as a distinct dataset, but the server would benefit from some consolidation or a higher-level catalog tool.

Completeness4/5

As a read-only datasets API, the surface is broadly complete: status checking, pagination, and one coverage-map companion exist where needed. Minor gaps include the absence of a global dataset catalog/coverage listing and the lack of companion list tools for most other datasets.

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