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Alphanume Datasets

De-SPAC Lifecycle

get_de_spac_events
Read-onlyIdempotent

De-SPAC lifecycle: one row per SPAC business combination tracked from the definitive-agreement announcement through registration, definitive proxy, shareholder vote, exchange listing and completion (or termination / trust liquidation). Every row carries the SPAC's own symbol (prior_ticker), the combined company's symbol as soon as a filing names it (new_ticker -- the definitive proxy does so in most deals, weeks before the close), warrant ticker, exchange, SPAC / target / new company names, and the dated milestones: announced_date, meeting_date, record_date, vote_approved_date, listing_filed_date, expected_close_date and expected_trading_date (each with a *_basis column saying whether the date was stated in a filing or derived), closing_date, trading_commencement_date, terminated_date, plus next_event_date / next_event_type for what comes next. Completed rows also carry the closing financials (redemption shares and dollars, trust remaining, PIPE, gross proceeds, pro forma shares, enterprise and equity value), a confidence score and the supporting evidence quote. date is the SEC filing date of the filing that set the row's current status (for completed deals: the closing 8-K). ticker is the symbol trading for the deal today (new_ticker once listing is pending, otherwise prior_ticker). Fields are null where no filing supported them; quality_flags lists any rule that altered or withheld a value.

Use status=pending&upcoming=true to see de-SPACs before the new symbol trades, ticker= to follow one deal, view=history to replay what was known on a given date, or status=completed with a date range for the classic redemption-rate event study.

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.
viewNo'latest' (default): one row per deal with its current status. 'history': the append-only status log, one row per transition (deal_id, status, date = filing date, created_at = when the pipeline first recorded it) for point-in-time replays.
statusNoComma-separated deal statuses to return: announced, registration_filed, registration_effective, vote_scheduled, approved, listing_pending, completed, terminated, liquidated; or the shorthands 'all' (default) and 'pending' (every non-terminal status). Unknown values return a 400 listing the valid ones.
tickerNoSymbol filter matched against BOTH the SPAC's own symbol (prior_ticker) and the post-combination symbol (new_ticker), so either side of the rename works.
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.
upcomingNo'true' = only pending deals with a future milestone (next_event_date >= today); 'false' = everything else. Omit for both.
updated_sinceNoOnly rows updated at or after this timestamp (YYYY-MM-DD or YYYY-MM-DD HH:MM:SS); for incremental syncs.
next_event_lteNoYYYY-MM-DD: only deals whose next milestone (meeting, expected close or expected first trade) is on or before this date.

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?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior; the description adds substantial context beyond those: the meaning of date as the SEC filing date, ticker as the current trading symbol, null-field semantics, quality_flags behavior, and 403 error interpretation. This gives the agent a clear model of how data behaves without contradicting 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 long but densely packed with necessary details that the absent output schema would otherwise leave uncovered. It is well-structured: a lifecycle/field paragraph, a data-semantics paragraph, and an actionable usage paragraph. It could be slightly tightened, but every major sentence adds information an agent needs.

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?

Given the tool's complexity, 12 optional parameters, rich annotations, and no output schema, the description is complete: it explains row granularity, all milestone and financial fields, null/quality handling, point-in-time history, common query patterns, and API-key/error behavior. An agent can select and invoke this tool correctly with no further context.

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 descriptions cover 100% of the 12 parameters, so the baseline is 3. The description adds meaning by tying key parameters to use cases (status+pending+upcoming, view=history) and clarifying derived dates and the ticker rename behavior. It does not simply repeat the schema, earning an above-baseline score.

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 states a specific verb-resource pair: it returns one row per SPAC business combination tracked across a defined lifecycle. It distinguishes itself from the many sibling get_* tools by naming the exact domain (De-SPAC) and the full list of milestones and financial fields returned.

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 gives four concrete usage recipes: status=pending&upcoming=true, ticker=<symbol>, view=history, and status=completed with a date range for redemption-rate studies. It clearly explains when each is appropriate, though it does not explicitly state when not to use this tool versus a sibling; the sibling topics are sufficiently different that this is a minor gap.

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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