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ShearQuery — Barber & Beauty Industry Data

Count Texas barber & cosmetology licensees

texas_licensee_counts
Read-only

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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

The annotation readOnlyHint=true already declares this as a safe read operation, so the description doesn't need to repeat that. The description adds the context that the number isn't published elsewhere, which is useful, but it doesn't disclose any additional behavioral traits like rate limits, data freshness, or pagination. Given the annotation coverage, a 3 is appropriate—it adds some value but not rich behavioral detail.

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, no fluff. The primary action and filters are front-loaded, and the second sentence justifies the tool's existence. Every word earns its place.

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?

The tool has two optional parameters fully documented in the schema, annotations cover the read-only nature, and no output schema is present. The description explains the use case and what result to expect (a count). There are no missing pieces an agent would need to call 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% for both parameters, so the schema already explains their meaning. The description reinforces the purpose of expiring_before ('to size who is affected by a change taking effect on that date'), which adds slight nuance but doesn't fundamentally exceed the schema. Baseline 3 is correct.

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') with a clear resource ('active Texas licensees from the TDLR public record') and adds the filtering dimension (by licence type, expiry date). It also provides the real-world purpose (sizing affected populations for rule changes), which distinguishes it from sibling tools like verify_texas_license or compare_barbershops_salons.

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 implies when to use it: when you need to know how many licensees a change affects, and notes the number isn't published elsewhere. It doesn't explicitly list alternatives or exclusions, but the sibling tools are clearly different in scope (auditing, comparing, verifying), so the context is sufficient for an agent to select this tool for counting tasks.

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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