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Company Stats Aggregations

company_stats
Read-onlyIdempotent

Return aggregate counts across the 3.3M-entity registry — by country, jurisdiction, industry, entity_type, employee_range, decade founded, plus hierarchy/data-quality coverage. Filter the slice with country/jurisdiction/industry/naics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoItems returned per facet (1-50).
countryNoISO 3166-1 alpha-2 country, e.g. 'US'.
industryNoIndustry keyword.
naics_codeNo2-digit NAICS sector code.
jurisdictionNoISO 3166-2 jurisdiction, e.g. 'US-DE'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive hints. The description adds value beyond those by disclosing the behavioral trait that it returns only aggregate counts (not entity-level data) across defined facets, and that the registry is 3.3M entities. No contradictions with annotations.

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?

Two tightly written sentences. The first front-loads the action and resource, the second specifies filter parameters. No filler or repetition of schema details.

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?

With no output schema, the description adequately conveys the return nature (aggregate counts per facet) and the available filters. It does not detail the exact output structure or what 'hierarchy/data-quality coverage' includes, but for a stats tool with safe annotations this is a minor gap.

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 description coverage is 100%, so the baseline is 3. The tool description goes further by explaining the role of country/jurisdiction/industry/naics as slice filters, and implicitly clarifies that entity_type, employee_range, and decade founded are facet dimensions rather than filter parameters — useful semantic context beyond the individual property descriptions.

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 opens with a specific verb and resource: 'Return aggregate counts across the 3.3M-entity registry,' and enumerates the exact facet dimensions (country, jurisdiction, industry, entity_type, employee_range, decade founded, plus hierarchy/data-quality coverage). This clearly distinguishes it from sibling tools like company_enrich or company_search, which return individual entity data rather than aggregations.

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 provides clear context for when this tool is appropriate: when aggregate counts over the registry are needed, not individual records. It also gives filter usage ('Filter the slice with country/jurisdiction/industry/naics'), but does not explicitly name alternatives or state when-not-to-use, so it stops short of a 5.

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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