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Alphanume Datasets

by com.alphanume

Shelf Registrations (S-3/F-3)

get_shelf_registrations
Read-onlyIdempotent

Shelf registrations: every S-3/F-3 shelf registration statement, amendment and automatic (WKSI) shelf, normalized into a point-in-time capacity ledger -- the upstream signal behind every dilution screener. One row per filing: ticker, cik, company name, form, shelf_type (new|amendment|automatic), capacity_amount in USD (NULL means an indeterminate/automatic shelf -- never a guessed number), securities covered, WKSI flag, is_resale (selling-stockholder registrations), EFFECT date, days_to_effective, a 3-year expiry estimate, 424B5 takedown count, and market cap / shares outstanding at filing.

Authorization is not issuance: capacity_amount is what the shelf REGISTERS, not what was sold. Screen date_gte over recent weeks for fresh shelf capacity, filter shelf_type=new + is_resale=0 rows for primary dilution risk, or pull one ticker's history. Pagination: results are capped at 50,000 rows per request; when the response has has_more=true, pass next_cursor's date and accession_number back as cursor_date and cursor_accession 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
cikNoSEC CIK number (digits only) to filter by issuer.
dateNoExact date, YYYY-MM-DD. Cannot be combined with the date range parameters.
formNoExact form type: S-3, F-3, S-3/A, F-3/A, S-3ASR or F-3ASR.
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.
shelf_typeNoShelf category: new, amendment, or automatic (WKSI ASR shelves).
cursor_dateNoPagination: the 'date' value from the previous response's next_cursor. Must be sent together with cursor_ticker.
updated_sinceNoOnly rows whose last_updated is on/after this date (YYYY-MM-DD) -- catches EFFECT/takedown refreshes.
cursor_accessionNoAccession number from next_cursor for keyset pagination (pass with cursor_date).

Schema Changelog

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

  1. First observed

TDQS

A5/5.0
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, and the description adds substantial behavioral context: row semantics, capacity_amount NULL meaning an indeterminate shelf, the 50,000-row pagination cap with next_cursor handling, and 403 error semantics. This goes well beyond what annotations alone 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?

Although long, the description is densely informative with no filler. It front-loads the core purpose and row semantics, then covers usage patterns, pagination, and error handling in a logical order. Every sentence adds operational value.

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 fully compensates by enumerating all returned fields, explaining NULL capacity semantics, describing pagination behavior, and covering authentication/error cases. An agent has enough context to call the tool correctly and interpret its response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

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 usage-level parameter semantics beyond the schema: using date_gte for recent shelf capacity, combining shelf_type=new with is_resale=0 for dilution risk, and passing next_cursor values back as cursor_date and cursor_accession. This materially helps an agent select and combine parameters correctly.

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 clearly identifies the tool as exposing S-3/F-3 shelf registration filings normalized into a point-in-time capacity ledger, with a specific row-level schema. It distinguishes itself from downstream dilution screeners by stating it is the upstream signal, so an agent can differentiate it from sibling tools like get_dilution_filings without ambiguity.

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?

The description gives concrete usage patterns: screening date_gte for fresh shelf capacity, filtering shelf_type=new and is_resale=0 for primary dilution risk, or pulling a single ticker's history. It also explicitly warns that authorization is not issuance, preventing misuse, and provides pagination and error-handling instructions.

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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