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ShearQuery — Barber & Beauty Industry Data

by com.innergcomplete

Compare barber & cosmetology schools by exam pass rate

compare_barber_cosmetology_schools

Rank barber or cosmetology schools by real 2026 state licensing exam outcomes — written and practical pass rates, first-attempt rate, average attempts to pass, students tested, and tuition. Optionally filter to one city. This data is not published by Google, school websites, or review sites. Schools with fewer than 5 recorded test-takers are excluded because a percentage from a handful of students is not meaningful.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoOptional city filter, e.g. "Houston". Matched case-insensitively.
limitNoHow many schools to return (1-50, default 10).
licenseYesWhich exam's outcomes to rank on. Barber and Cosmetology are separate licences with separate exams; a school running both appears under both with its own results for each.

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are present, so the description carries the burden. It discloses the data provenance (2026 state licensing exam outcomes), the exclusion of schools with fewer than 5 test-takers, and the rationale. This is meaningful behavioral context beyond the schema, though it doesn't cover sorting details or response format.

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, front-loaded with the action and resource. Every sentence adds value: action/metrics, optional filter, data uniqueness and exclusion rule. No fluff.

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?

No output schema exists, so the description must explain return values. It lists all key metrics (written/practical rates, first-attempt, attempts, students tested, tuition) and the exclusion rule. This is sufficient for a ranking tool and covers filters and context.

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 the baseline is 3. The description adds minimal extra meaning beyond the schema: it reiterates city filtering but doesn't detail license or limit semantics. Therefore, a score 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?

Specific verb 'Rank' identifies the action, and the resource is clearly barber or cosmetology schools. It lists concrete output metrics (pass rates, tuition) and differentiates from sibling tools about barbershops/salons and licensee counts.

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?

Description provides clear context: it ranks schools by exam outcomes and optionally filters by city. It does not explicitly state when to use versus alternatives, but the data-source note ('not published by Google...') implies authoritative use. No explicit exclusions are given, so it earns 4.

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

All ShearQuery — Barber & Beauty Industry Data toolsInspect
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TDQS

A4.1/5.0
Disambiguation5/5

Each tool addresses a completely different data domain—school rankings, shop booth rent, and licensee counts. There is no semantic overlap, so an agent can confidently select the right tool without ambiguity.

Naming Consistency4/5

The first two tools follow a clear 'compare_' verb-noun pattern, but the third tool uses a noun phrase ('texas_licensee_counts') instead of a verb-based convention. The names are still intuitive and readable, but the pattern is not fully consistent.

Tool Count5/5

Three tools is exactly right for a niche data server focused on barber and beauty industry analytics. Each tool provides a distinct, high-value query, and the count keeps the surface area minimal and easy to navigate.

Completeness4/5

The server covers its core stated purpose well, offering comparison and counting for three major data categories. Minor gaps exist—such as no direct detail-lookup for individual schools or shops—but these are workable since the compare tools return ranked results with the key information.

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