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Alphanume Datasets

by com.alphanume

SEC Filing Intensity

get_filing_intensity
Read-onlyIdempotent

SEC filing intensity: the number of EDGAR filings each company submitted per day. Corporate action leaves fingerprints in the filing stream -- financings, restructurings, activist involvement, and deal activity all show up as bursts of filings before the news is digested. One row per ticker per day: ticker, company name, date, filing_count.

Screen filing_count_gte over recent dates to surface names with unusual filing bursts, or pull one ticker's history to spot regime changes in its filing cadence. 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
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.
filing_count_eqNoExact filing count match. Cannot be combined with the filing_count range parameters.
filing_count_gtNoOnly rows with filing_count > this value.
filing_count_ltNoOnly rows with filing_count < this value.
filing_count_gteNoOnly rows with filing_count >= this value (non-negative).
filing_count_lteNoOnly rows with filing_count <= this value.

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Even with readOnly, openWorld, idempotent, and non-destructive annotations, the description adds meaningful behavioral detail: a 50,000-row per-request cap, cursor-based pagination semantics, the need for an Alphanume Pro API key, and the specific meaning of 403 errors. This goes well beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, front-loaded with the core definition, then usage, pagination, and error context. The 'corporate action fingerprints' sentence adds useful intuition and is not excessive; each remaining sentence carries operational weight.

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 14-parameter tool with no output schema, the description covers what an agent needs: row schema, pagination mechanics, request-size limits, authentication requirements, and error interpretation. This is a high-complexity tool, and the description is sufficiently complete.

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. The description adds value by showing how parameters are meant to be used, e.g., screening with filing_count_gte, pulling ticker history, and using cursor_date/cursor_ticker for pagination. It also clarifies that max_rows should not be raised as a substitute for narrowing filters.

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 defines the resource: the number of EDGAR filings per company per day, with an explicit row shape (ticker, company name, date, filing_count). This distinguishes it from sibling tools by domain (SEC filing cadence) and by data granularity, so an agent knows exactly what it returns.

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?

It gives concrete use cases: screening filing_count_gte over recent dates for unusual filing bursts, or pulling one ticker's history for regime changes. It provides actionable guidance for parameter selection and pagination, but it does not explicitly contrast with sibling tools or state when not to use it.

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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