ShearQuery — Barber & Beauty Industry Data

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.

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.

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.

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.

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.

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.

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

With no annotations, the description carries the full burden. It reveals that booth rent is 'quoted directly by shops rather than scraped,' coverage is partial, and the response states how many listings publish a rate. This is valuable behavioral context not available elsewhere. It doesn't mention side effects, but the read-only nature is implied.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core function and criteria. The second sentence adds a necessary caveat without fluff. No wasted words.

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

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

Given no output schema, the description hints at the response by noting it states coverage counts. The tool's purpose, inputs, and key behavioral caveat are covered. It could be slightly more explicit about the output format, but it's complete for a query tool with well-defined parameters.

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

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 context about the meaning of the results (chairs available, booth rent) but does not elaborate on the parameters themselves, which are already well-documented in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action: 'Find barbershops and salons ranked by weekly booth rent' with specific criteria (chairs available, Google rating, review count, hiring status). It distinguishes itself from siblings by focusing on shop comparison rather than school or licensee data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context: 'Answers what a chair costs in a given city and which shops have one free.' It doesn't explicitly mention when not to use it or alternatives, but the use cases are well-defined, justifying a 4 rather than 5.

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

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.

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.

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.

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.

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.

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.