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Alphanume Datasets

by com.alphanume

Optionable Tickers

get_optionable_tickers
Read-onlyIdempotent

Historical optionable universe: point-in-time snapshots of which US equities had listed options on each snapshot date, with the average number of days between listed expirations (avg_days_between -- lower means a denser expiration calendar) and a has_weeklies flag. Essential for honest options backtests: it tells you what was actually tradable then, not what is optionable today.

Filter by snapshot date range; omit filters for the most recent snapshots first. Pagination: results are capped at 50,000 rows per request; when the response has has_more=true, pass next_cursor's date and ticker back as cursor_date and cursor_ticker to fetch the next page.

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
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.
cursor_dateNoPagination: the 'date' value from the previous response's next_cursor. Must be sent together with cursor_ticker.
cursor_tickerNoPagination: the 'ticker' value from the previous response's next_cursor. Must be sent together with cursor_date.

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 as read-only, idempotent, and non-destructive. The description adds substantial behavioral context beyond that: 50,000-row pagination cap, has_more/next_cursor mechanics, and crucially explains that 403 PRO_SUBSCRIPTION_REQUIRED or DATE_RANGE_RESTRICTED errors indicate plan coverage, not missing data.

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?

Every sentence earns its place: core concept, field definitions, filtering guidance, pagination mechanics, and error semantics. The most important point-in-time distinction is front-loaded, and the structure flows logically from what to how to error handling.

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?

Despite lacking an output schema, the description names the key output fields (avg_days_between, has_weeklies), explains pagination response elements (has_more, next_cursor), and covers auth requirements and error interpretation. This is sufficiently complete for an agent to call and process results correctly.

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 documents all 7 parameters, so the baseline is 3. The description adds extra meaning by explaining that cursor_date and cursor_ticker must be sent together and come from next_cursor, and by clarifying that max_rows is applied after the API responds. This goes beyond the schema's 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?

States exactly what the tool returns: historical point-in-time snapshots of US equities with listed options, including specific fields like avg_days_between and has_weeklies. The emphasis on 'what was actually tradable then, not what is optionable today' clearly distinguishes its purpose from any current-universe tool among siblings.

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?

Gives clear guidance on when to use it, such as 'essential for honest options backtests,' and how to apply date filters and pagination. It doesn't explicitly name when-not-to-use alternatives, but the context is strong enough to guide selection.

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.

Resources