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Alphanume Datasets

by com.alphanume

Corporate Default Events

get_corporate_default_events
Read-onlyIdempotent

Corporate default events labeled from SEC filings: answers "which public companies just defaulted on an obligation, and on what terms?" One row per default event: ticker, event date, source filing URL, plus enriched fields extracted from the filing text -- event type, the obligation's name and type, creditor name, principal outstanding, amount accelerated, missed payment amount, stated default date, grace period end, whether acceleration was declared, and a confidence score with the supporting evidence quote. Fields are null where the filing text did not explicitly support them.

Use it for distressed screening, credit event studies, or monitoring a date range for fresh defaults.

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.
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.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses that fields are null when the filing text does not explicitly support them, that an API key is required, and that specific errors indicate plan limitations rather than missing data. This materially helps the agent interpret results and troubleshoot failures.

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 front-loaded with the core purpose and row structure, followed by use cases and error semantics. Every sentence contributes useful information and there is no filler or redundancy.

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, the description compensates by listing the returned fields in detail, explaining null behavior, and covering error semantics. Combined with full schema coverage for parameters and clear annotations, an agent has everything needed to select and invoke the tool correctly.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already explains each date parameter and max_rows. The description reinforces the date-range monitoring use case but does not add parameter-specific meaning beyond what the schema provides, matching the baseline expectation.

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 names a specific resource (corporate default events from SEC filings) and answers the exact question the tool addresses: which public companies just defaulted and on what terms. It enumerates the row structure and enriched fields, clearly distinguishing it from the finance-focused 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 Guidelines4/5

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

The description states explicit use cases: distressed screening, credit event studies, and monitoring date ranges for fresh defaults. It does not name alternatives or explicitly say when not to use the tool, but there is no obvious sibling with overlapping functionality, so the context 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.

Resources