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Alphanume Datasets

by com.alphanume

Earnings Move History

get_earnings_move_history
Read-onlyIdempotent

Earnings move track record: answers "does the options market systematically over- or under-price this name's earnings moves?" One row per earnings event per ticker: the pre-earnings ATM straddle price and implied move (% and $), ATM IV, the realized post-earnings return and absolute move, the over/under-pricing gap and move ratio, an overpriced flag, EPS estimate vs actual, plus running per-ticker history stats (events to date, hit rate of straddle overpricing, average implied vs realized move to date).

Use it before an earnings trade: query ticker to see whether selling or buying the earnings straddle has historically paid on that name, or screen a date to review how yesterday's reporters resolved. date is the earnings date.

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.

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?

Annotations already mark this read-only, idempotent, open-world, and non-destructive, and the description does not contradict them. It adds substantial behavioral context: one row per earnings event per ticker, the meaning of the 403 errors (plan limitation, not missing data), and the Pro API key requirement. This goes well beyond what annotations alone communicate.

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 detailed but every sentence earns its place. It front-loads the core purpose, lists the output fields compactly, gives direct usage instructions, and closes with error interpretation. There is no filler or redundancy.

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 seven optional params, no output schema, and a large sibling set, the description covers the essentials: what the data represents, what fields are returned, how to use ticker and date, the auth requirement, and how to interpret errors. The input schema handles filter mechanics, so nothing critical 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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful domain context: it tells agents that ticker is how you evaluate historical straddle profitability for a name and clarifies that date means the earnings date. This enriches the parameter semantics beyond the raw schema descriptions.

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 specific resource ('earnings move track record') and a precise diagnostic question: whether the options market systematically over- or under-prices earnings moves. It also enumerates the output in concrete terms (straddle price, implied move, realized move, overpricing gap, flag, EPS data), which makes it unambiguously distinct from siblings like get_iv_hv_premium or get_momentum_index.

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 gives explicit, actionable usage guidance: use it before an earnings trade by querying ticker to assess whether selling or buying the straddle has paid, or screen by date to review recent reporters. It does not explicitly name alternative tools or when-not-to-use conditions, but the intended context is clear enough.

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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