Alphanume Datasets

Vol-of-Vol Ranking

get_vol_of_vol

Read-onlyIdempotent

Vol-of-Vol Index: answers "how unstable is this name's volatility itself?" For each US optionable equity, per trading day: the coefficient of variation of its ~30-day implied vol (iv_vov) and 20-day realized vol (hv_vov) over the trailing month (std/mean of the last 21 observations), the underlying trailing mean and std, and a daily cross-sectional ranking of the most vol-unstable names. Rows update intraday and settle after the close (is_final=1).

High vol-of-vol names are where vega risk is most treacherous (and where vol dislocations appear); low vol-of-vol names have sticky, well-behaved vol surfaces.

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

Name	Required	Description
`date`	No	Exact date, YYYY-MM-DD. Cannot be combined with the date range parameters.
`ticker`	No	Ticker symbol filter, e.g. 'AAPL'. Case-insensitive.
`date_gt`	No	Start of date range, exclusive (YYYY-MM-DD).
`date_lt`	No	End of date range, exclusive (YYYY-MM-DD).
`date_gte`	No	Start of date range, inclusive (YYYY-MM-DD).
`date_lte`	No	End of date range, inclusive (YYYY-MM-DD).
`max_rows`	No	Maximum 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.
`max_hv_vov`	No	Only rows with hv_vov <= this value.
`max_iv_vov`	No	Only rows with iv_vov <= this value.
`min_hv_vov`	No	Only rows with hv_vov >= this value (>= 0).
`min_iv_vov`	No	Only rows with iv_vov >= this value (>= 0).
`only_final`	No	If true, return only settled end-of-day rows (is_final=1). By default the latest value is returned, which intraday may be provisional.
`min_hv_vov_rank`	No	Only rows whose realized vol-of-vol sits at or above this cross-sectional percentile for the day, in [0, 1].
`min_iv_vov_rank`	No	Only rows whose implied vol-of-vol sits at or above this cross-sectional percentile for the day, in [0, 1].

Schema Changelog

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

First observedSep 6, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnly/idempotent/destructive annotations, the description discloses intraday update behavior, settlement after close via is_final=1, authentication requirements (Pro API key), and interprets 403 errors as plan limitations rather than missing data. This is exactly the kind of behavioral context that structured annotations cannot convey.

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 well-structured: definition and data shape first, then interpretive guidance, then auth/error caveats. Every sentence adds value, and the most important information is front-loaded.

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 14-parameter, fully optional tool with no output schema, the description is remarkably complete. It explains the underlying computed fields, the ranking dimension, the intraday vs final distinction, and the error semantics. An agent can safely invoke and interpret this 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?

The input schema documents every parameter, so the baseline is 3. The description adds meaningful context by defining what iv_vov and hv_vov actually measure, tying those definitions to the filter parameters and to the cross-sectional rank fields. This helps the agent understand threshold meaning beyond the raw schema.

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 names a precise resource (vol-of-vol for US optionable equities), defines the core metric (coefficient of variation of implied and realized vol over a trailing month), and clearly separates it from related concepts like iv_rank or iv_hv_premium. It is not tautological and gives the agent a concrete sense of the data shape and ranking.

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 provides clear analytical guidance: high vol-of-vol names indicate treacherous vega risk and potential vol dislocations, while low vol-of-vol names have sticky surfaces. It does not explicitly name alternative tools or state when NOT to use this tool, but the use case is strongly implied.

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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