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Alphanume Datasets

by com.alphanume

Dividend-Capture Calendar

get_dividend_capture
Read-onlyIdempotent

Dividend-capture calendar and scorecard: answers "which ex-dividend events are coming up, and how has capturing this name's dividend actually worked?" One row per ex-dividend event: the full schedule (declaration/record/pay dates, cash amount, frequency, type, annualized dividend, capture yield), the ex-day price action (cum-day close, ex open/close, price drop and drop ratio at open and close, net capture %), and the recovery record (breakeven price, recovery status/date, days to recover, recovered-within 1/3/5/10/20-day flags). A drop ratio well below 1 means the stock historically falls less than the dividend -- the raw material of a capture strategy.

Set upcoming=true for the forward ex-div calendar (default 7 days out, up to 120 via future_days); use ticker for one name's full capture history.

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.
upcomingNoIf true, return only the FORWARD calendar: ex-dividend dates after today, out to future_days ahead. Overrides date range filters.
future_daysNoForward horizon in calendar days for the upcoming calendar (default 7, max 120).
recovery_statusNoFilter by the recovery_status label of the ex-div event (values as returned in the data, e.g. whether the price has recovered to breakeven yet).

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?

The annotations already mark this as read-only, idempotent, and non-destructive. The description adds substantial behavioral context: the exact output row structure, interpretation guidance for drop ratio, requirement for a Pro API key, and the meaning of specific error codes. This goes well beyond what the annotations alone provide.

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 carries distinct value: purpose, output structure, mode selection, interpretation guidance, and error handling. It is front-loaded with the core purpose and avoids redundancy with the schema.

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?

With no output schema present, the description carries the full burden of explaining return values, and it does so thoroughly: row granularity, schedule fields, price-action fields, recovery flags, and interpretation. It also covers authentication requirements and error semantics, making the tool callable without external documentation.

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 already documents all 10 parameters with descriptions, so the baseline is 3. The description adds useful semantics beyond the schema, such as how upcoming=true and future_days define the forward calendar, how ticker maps to full capture history, and how to interpret recovery-related output. This lifts the score slightly above baseline.

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 explicitly identifies the resource as a dividend-capture calendar and scorecard, including the exact questions it answers and the output granularity (one row per ex-dividend event). This clearly distinguishes it from sibling tools, which cover unrelated market or event data.

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 mode instructions: set upcoming=true for the forward calendar and use ticker for a single name's capture history. It also explains that 403 errors indicate plan restrictions rather than missing data. It does not name alternative sibling tools, but the tool is distinct enough that the mode guidance is sufficient.

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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