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Alphanume Datasets

by com.alphanume

Historical Market Cap

get_historical_market_cap
Read-onlyIdempotent

Point-in-time historical market capitalization: daily market_cap and shares_outstanding per US ticker, as they were known on each date (no restatement, no survivorship bias). The backbone reference for size filters, cap-weighted baskets, and normalizing anything by company size in a backtest.

Requirements: provide ticker OR at least one date filter. A single date with no ticker returns the whole market for that day; a date RANGE without a ticker is capped at 7 calendar days. 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.

Companion tool: list_market_cap_tickers shows which tickers exist and their first available 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.
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

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses significant behavior: no restatement, no survivorship bias, 50,000-row pagination cap, has_more/next_cursor mechanics, and the 7-day range limit. It also explains that PRO_SUBSCRIPTION_REQUIRED and DATE_RANGE_RESTRICTED errors mean the plan does not cover the request rather than 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?

Three structured paragraphs front-load the core semantics, then move through requirements, pagination, companion tools, and auth/error handling. Every sentence contributes operational or selection value with no filler.

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 9-parameter read tool with no output schema, the description supplies return fields (market_cap, shares_outstanding), pagination details, limits, companion tool guidance, and error interpretation. No critical operational gap remains for an agent to invoke it 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?

Schema already covers all 9 parameters with descriptions, so the baseline is 3. The description adds meaning beyond the schema: the ticker-or-date-filter requirement, the cursor_date/cursor_ticker coupling rule, pagination behavior, and the range cap, which is valuable operational context the schema alone does not convey.

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?

Description names a specific verb and resource: point-in-time historical market capitalization with daily market_cap and shares_outstanding per US ticker. It also states the key data semantics (no restatement, no survivorship bias), and names the companion tool list_market_cap_tickers, distinguishing it from related sibling tools.

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

Usage Guidelines5/5

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

Explicit invocation rules are provided: provide ticker OR at least one date filter, a bare date returns the whole market, and a date range without ticker is capped at 7 days. It also routes agents to list_market_cap_tickers for checking ticker availability and clarifies 403 error meaning, giving strong when-to-use and troubleshooting guidance.

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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