Skip to main content
Glama

Alphanume Datasets

by com.alphanume

S-1 Dilution Filings

get_dilution_filings
Read-onlyIdempotent

S-1 dilution filings tracker: answers "which companies are registering new share supply, and where is each registration in its lifecycle?" One row per S-1 registration event: ticker, company name, filing timestamp, market cap at filing, whether the filing is dilutive and/or a resale, shares offered, whether/when it became effective (with days-to-effective), whether/when it was withdrawn, and the SEC accession number + filing URL for the source document.

Use it to flag dilution overhang on small caps, track time-to-effectiveness, or build event studies around registration filings. Filters by ticker and filing-date range (no exact-date parameter on this dataset).

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
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.

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context beyond annotations: API key requirements, and the meaning of 403 errors (subscription/date-range restriction rather than missing data). This meaningfully helps agents interpret failures correctly.

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 front-loaded with the tool's purpose and output shape, then gives use cases, filter limitations, and error semantics. It is somewhat long due to the field enumeration, but every part contributes useful information and nothing feels redundant or padded.

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 fully enumerating the returned row fields. It also covers authentication, error interpretation, filter boundaries, and practical use cases. An agent has enough information to invoke the tool correctly and interpret the response meaningfully.

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?

The input schema already covers all parameters with descriptions, so the baseline is 3. The description adds useful parameter semantics by stating that filtering is limited to ticker and filing-date range and explicitly calling out that there is no exact-date parameter. This helps an agent choose date filters correctly without over-specifying.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the resource as S-1 dilution filings and specifies the exact row-level content (ticker, filing timestamp, dilutive/resale flags, effectiveness/withdrawal status, SEC links). It is distinct from generic filing tools, but it does not explicitly differentiate from the closely related sibling get_shelf_registrations, so it falls just short of full sibling differentiation.

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 provides explicit use cases: flagging dilution overhang, tracking time-to-effectiveness, and building event studies. It also clarifies the available filter scope (ticker and filing-date range, no exact-date parameter). It does not state when to prefer a sibling tool, but the context is clear enough for basic routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Alphanume Datasets toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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