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mambalabsdev

Public Company Reporting Window Finder

by mambalabsdev

Build Company Universe

build_company_universe
Read-onlyIdempotent

Filter public companies by exchange, region, sector, and more to build a custom universe. Returns 44 fields per company, using criteria instead of identifiers.

Instructions

Build a list of listed companies from filters rather than from identifiers you already hold. Filter by exchange, country, region, sector, security type, public float band, fiscal year end month, reporting cadence, operating status and foreign private issuer status. Returns 44 fields per company, the same shape resolve_company returns. At least one filter is required, so a bare call cannot pull the whole universe. This tool does NOT accept company identifiers: use resolve_company, qualify_company or get_reporting_timing for specific companies. SET limit EXPLICITLY. It defaults to 1000 and you are charged per company row returned, so an unbounded call is an expensive call. Truncation is always reported, never silent. Requires an APIFY_TOKEN and consumes Apify credits. Read only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows for universe and season. Truncation is always reported, never silent.
regionsNoShorthand for a set of venues and countries: us, uk, eu. Widens an explicit exchange or country filter rather than replacing it.
sectorsNoSEC SIC descriptions, e.g. Pharmaceutical Preparations. Populated on roughly 65 percent of the publishable universe.
cadencesNoquarterly, semiannual, annual, unknown.
country_codesNoISO 3166-1 alpha-2, e.g. US, GB, FR.
exchange_codesNoISO 10383 MICs. 18 venues are covered.
security_typesNoordinary_shares, depositary_receipt, preferred_shares.
public_float_bandsNomicro, small, mid, large, mega, unknown. Size runs on public float because market capitalization is not populated anywhere in this dataset.
us_registrant_onlyNoKeep only companies carrying an SEC CIK.
exclude_share_alikeNoDrop rows derived from CC BY-SA sources, whose share alike condition may not suit a closed product.
exclude_blank_checksNoDrop pre deal SPACs. Separate from the operating company filter: a blank check shell is flagged as an operating company and passes every ordinary firmographic filter.
fiscal_year_end_monthsNoIntegers 1 to 12. Fiscal year end is effectively a United States field in this dataset.
foreign_private_issuerNoFilter on foreign private issuer status.
operating_companies_onlyNoDrop funds, trusts and other non operating entities. Sent as a string for Clay compatibility.
exclude_name_only_matchesNoDrop rows whose identity link rests on a name and country agreeing rather than on an identifier. Use this wherever a wrong identity link matters.
min_provenance_confidenceNoSet to high to exclude rows whose source terms were never read.
exclude_december_fiscal_year_endNoKeep only companies whose fiscal year ends in a month other than December, the accounts whose budget cycle is out of phase with a calendar quarter.

Schema Changelog

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

  1. First observedv1.0.0

TDQS

A5/5.0
Behavior5/5

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

The description goes well beyond the annotations (which already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint). It discloses: minimum one filter required, default limit of 1000 rows, per-row charging, truncation reporting behavior (never silent), APIFY_TOKEN requirement, and Apify credit consumption. This fully characterizes operational behavior.

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 key purpose, followed by essential usage constraints and behavioral details. Every sentence adds distinct value—no fluff or repetition. It efficiently covers purpose, alternatives, cost implications, and special notes in under 150 words.

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?

Despite the high parameter count (17) and no output schema, the description is remarkably complete. It notes the output structure (44 fields, same shape as resolve_company), explains truncation reporting, and covers all key behavioral constraints. The annotations further support completeness by indicating idempotent and read-only behavior. No obvious gaps remain for an agent to fail.

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?

Even though schema description coverage is 100%, the description adds substantial context beyond the schema. For example, it states that regions widen rather than replace filters, public_float_bands are based on public float because market cap is unavailable, fiscal_year_end_months is primarily a US field, and security_types are specifically enum values. These enrichments justify a top 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 clearly states the tool builds a list of listed companies using filters, not identifiers. It uses a specific verb ('Build') and resource ('list of listed companies'), and explicitly distinguishes from sibling tools by noting it does not accept company identifiers and directing users to resolve_company, qualify_company, or get_reporting_timing for specific companies.

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 provides explicit guidance on when to use this tool (filter-based universe building) and when not to (company identifier lookups). It names alternative tools (resolve_company, qualify_company, get_reporting_timing) and includes a critical usage warning about setting the limit explicitly to control costs.

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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