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private_geography

Geographic distribution of private businesses. Group by city, zip, county, or state. Shows where businesses are concentrated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoTop N locations (default 30)
stateNoFilter by state
group_byNoGroup by: city, zip, county, statecity

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden, but it only states the high-level function. It does not disclose whether the tool is read-only, how results are sorted, default limit behavior, or what the return structure looks like. This is a significant transparency gap.

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 sentences with no unnecessary words. The key information is front-loaded, and the structure is efficient.

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

Completeness3/5

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

For a simple 3-parameter tool with no annotations and no output schema, the description covers the basic purpose but omits expected output details and behavioral nuances. It is minimally viable but has clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all parameters are already documented. The description adds no new meaning beyond restating the group_by options, which the schema already lists. Baseline of 3 is appropriate.

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?

Clearly states the tool provides geographic distribution of private businesses and lists grouping dimensions (city, zip, county, state). This distinguishes it from sibling tools like private_age_distribution and private_type_breakdown.

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?

Implies the tool is used for geographic analysis ('Shows where businesses are concentrated') but does not explicitly contrast with alternatives or specify when to choose this over other distribution tools. Clear context is present, so it earns a 4.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct query type: name search, identity resolution, attribute-based browsing, executive vs officer lookup, entity records, and aggregate statistics. Descriptions explicitly call out when to use one over another (e.g., private_search vs private_resolve vs private_browse).

Naming Consistency5/5

All tool names share the 'private_' prefix and follow snake_case. While some use verbs (browse, search, resolve) and others use nouns (entity, geography, stats), the pattern is highly predictable and the domain is uniformly private company data.

Tool Count5/5

11 tools is right-sized for a business registry server: enough to cover various lookup and analytical needs without redundancy. Each tool has a clear purpose and earns its place.

Completeness5/5

The surface covers the full lifecycle of interacting with company records: discover (search, browse, resolve), detail (entity), people (officer, ceo), signals (owner_operated), aggregations (age, geography, type), and dataset awareness (stats). No obvious gaps for the stated scope.

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