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ShearQuery — Barber & Beauty Industry Data

Count Texas barber & cosmetology licensees

texas_licensee_counts

Count active Texas licensees from the TDLR public record by licence type, optionally limited to those whose licence expires before a given date. Answers how many people a rule change, CE requirement or fee change actually affects — the number is not published anywhere in this form.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
license_typeNoOptional exact TDLR licence type, e.g. "Class A Barber", "Cosmetology Operator", "Cosmetology Manicurist", "Cosmetology Esthetician". Omit for a breakdown across all types.
expiring_beforeNoOptional ISO date (YYYY-MM-DD). Counts only licences expiring before it — use to size who is affected by a change taking effect on that date.

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of disclosing behavioral traits. It implies a read-only operation via the verb 'Count' and 'public record,' but it does not explicitly state that there are no side effects, permissions required, rate limits, or pagination behavior. The additional phrase about the number not being published adds purpose context but not operational transparency.

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 two sentences, with the first sentence front-loading the core function and the second adding a valuable purpose statement. Every sentence earns its place, with no filler or redundancy.

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?

Given the tool's simplicity (two optional params, no output schema, no annotations), the description is largely complete: it states the data source, the operation, the optional filters, and the real-world use case. It could explicitly mention the return format (single count vs. breakdown) but the schema's license_type description already covers this, making the description sufficient for an agent to invoke it correctly.

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 meaning beyond the schema—it mentions 'by licence type' and 'expires before a given date,' which the schema already covers with detailed descriptions. It does not introduce new semantic depth beyond what the structured parameter descriptions provide.

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 uses a specific verb ('Count') and identifies the exact resource ('active Texas licensees from the TDLR public record'), clearly distinguishing it from the sibling comparison tools. It also explains the practical purpose—quantifying how many people a rule change affects—which reinforces its unique role.

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 gives clear context on when to use the tool ('Answers how many people a rule change, CE requirement or fee change actually affects') and mentions the optional filter for expiring licences. It does not explicitly exclude alternatives or name sibling tools, but the use case is sufficiently clear for an agent to select it over comparison tools.

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

A4.1/5.0
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.