Skip to main content
Glama
Crawlora-org

Crawlora MCP

Official
by Crawlora-org
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

datasets_sec_companies_facets

Get aggregated counts of SEC companies by facet (SIC, exchange, revenue band, etc.) with optional filters like ticker, revenue, and financials. Use it to analyze distribution and refine searches.

Instructions

Facet the SEC companies dataset. Returns terms-aggregation counts for one facet of the SEC companies dataset, scoped to the same filters as search. Facet enum: sic, sic_description, exchange, state_of_incorporation, entity_type, reporting_currency, revenue_band, forms_filed. revenue_band buckets latest-annual revenue into: unknown, under_1m, 1m_10m, 10m_100m, 100m_1b, 1b_10b, over_10b.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over the company name, or an exact ticker match, max 256 characters
sicNoExact SIC industry-code filter, max 32 characters
facetYesFacet enum: sic, sic_description, exchange, state_of_incorporation, entity_type, reporting_currency, revenue_band, forms_filed
tickerNoExact ticker filter (case-insensitive), max 32 characters
exchangeNoExact exchange filter as reported by EDGAR, max 64 characters
form_filedNoExact form-type filter, e.g. 10-K, 8-K
entity_typeNoExact entity-type filter, max 64 characters
min_revenueNoMinimum latest-annual revenue in USD (normalized at reference rates), 0 or greater
has_financialsNoWhen true, keep only companies that have XBRL financial statements
reporting_currencyNoExact reporting-currency filter, ISO-4217 code, e.g. USD, JPY, EUR
state_of_incorporationNoExact state/country-of-incorporation filter, max 32 characters

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "sic",
+  "sic_description",
+  "exchange",
+  "state_of_incorporation",
+  "entity_type",
+  "reporting_currency",
+  "revenue_band",
+  "forms_filed"
+]
  2. Addedv1.6.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It clearly states the output is 'terms-aggregation counts' and explains the revenue_band bucket boundaries explicitly, which goes beyond a simple read hint. It doesn't mention pagination, limits, or response structure, but for a facet endpoint the core behavior is well covered.

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?

Three sentences with no redundant phrasing. The first sentence states the purpose, the second enumerates the facet options, and the third defines revenue_band buckets. Information is front-loaded and every sentence contributes unique detail. No filler or repetition of schema info.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a single required parameter, no output schema, and 100% schema coverage for optional filters. The description adequately explains the facet selection and provides the revenue_band bucket definitions. It could mention the shape of the returned counts or any limits, but the phrase 'terms-aggregation counts' gives a reasonable mental model. Overall, the description is sufficient for correct invocation.

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%, giving a baseline of 3. The description adds value by clarifying that all filter parameters behave identically to the search operation ('scoped to the same filters as search') and by defining the exact bucket names and ranges for revenue_band. This context is not inferable from the schema alone, so it meaningfully enhances parameter understanding.

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's function: 'Facet the SEC companies dataset' and 'Returns terms-aggregation counts for one facet'. It also names the exact facet enum and the special revenue_band buckets, leaving no ambiguity about what the tool does. It implicitly distinguishes from a search tool by referencing 'same filters as search' while emphasizing aggregation counts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

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

The description implies usage—use this for facet counts rather than full search—but doesn't provide explicit when-to-use vs. alternatives. It mentions 'scoped to the same filters as search' but never names a sibling or states conditions like 'use when you need counts, not full records'. No exclusions or when-not guidance is given, so it's implied usage only.

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

Deploy Server

Other Tools