JAWD Jaw Trainers

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

With readOnlyHint/destructiveHint/openWorldHint already declared, the description still adds real value: it discloses the return artifact (an external Shopify checkout link) and that 'nothing is charged in ChatGPT', which is exactly the side-effect information an agent needs before invoking. It does not cover failure modes or the per-line quantity cap, and the mention of an 'external' store sits in mild tension with openWorldHint=false, but the operational picture is largely complete.

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 short sentences, no filler, with the invocation condition front-loaded before the behavioral note and the exclusion. Every sentence carries a distinct piece of information.

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?

There is no output schema, but the description compensates by describing the return value (an external checkout link) and the charging behavior. Combined with the usage gating and domain exclusions, an agent has enough to call this correctly; only the line/quantity limits and error behavior are left implicit.

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% and the single nested 'lines' parameter is fully typed in the schema, so the baseline of 3 applies. The description adds no meaning beyond the schema - notably it never surfaces the quantity maximum of 5 per line or the 4-line cap, which are the constraints most likely to cause a failed call.

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 states a concrete outcome - 'Returns an external Shopify checkout link on JAWD's store' - which is the specific result of building a cart, and the trigger condition ('after the user picks a product and quantity and asks to buy') cleanly separates it from get_product, search_products, and recommend_routine. An agent can identify what this tool produces without opening the schema.

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?

Explicit when-to-use ('only after the user picks a product and quantity and asks to buy') and explicit when-not-to-use ('Do not use for medical jaw pain, TMJ, orthodontic, or dental questions'). Siblings are not named by name, but the positive trigger and the negative domain exclusions are strong enough to route the agent correctly.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds value annotations do not carry: it enumerates the returned data (current price, stock, image, product link), telling the agent what it can expect to surface.

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?

Two tight sentences: the positive trigger first, then the disambiguating exclusion. Every clause earns its place and there is no filler, though it is slightly terse on lookup semantics.

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?

With no output schema, the description compensates by naming the returned fields, and the annotations cover the safety profile. Missing only the boundary against search_products for non-specific queries; otherwise an agent has what it needs to call this 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% and the single 'sku' parameter has its own format examples (JAWD-L1-COBALT-30), so the schema does the heavy lifting. The description adds a small nuance by implying the input may be a product name rather than a raw SKU, but supplies no format or resolution guidance of its own.

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?

States a specific verb+resource (retrieve a product's price, stock, image, link) and even names the identifier type (SKU) used to look it up. It does not explicitly distinguish itself from the sibling search_products, which is the main confusion risk, so it stops short of a 5.

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?

Gives a clear trigger condition (user names a JAWD product or SKU) and a valuable explicit exclusion set (medical jaw pain, TMJ, orthodontic, dental), which disambiguates the 'JAWD'/'jaw' collision. It never names the alternative sibling tool (search_products) for the case where the user does not name a specific product, so no explicit alternative routing.

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?

Annotations already declare readOnlyHint, destructiveHint=false, and openWorldHint=false, and the description adds real scope constraints beyond that: 'General fitness guidance only; no medical, cosmetic-outcome, or weight-loss claims.' It stops short of saying what the returned routine looks like, but the guardrail content is genuinely additive.

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?

Two sentences, no padding, with the trigger condition front-loaded and the exclusions immediately after. Every clause carries routing or scope information.

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?

For a two-param, no-output-schema recommender with read-only annotations, the agent has the trigger, the exclusions, and the content limits it needs to choose and invoke the tool. The only gap is any hint of what form the recommended routine takes.

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 coverage is 100% with two enumerated parameters, so the schema does the documentation work. The description adds nothing about experience/goal semantics — notably the schema's own 'Shopping goal' label for the goal enum is confusing, but that is a schema issue the description does not resolve.

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 conveys that the tool answers 'how to start or structure jaw or face exercise', which is a clear intent statement, and the sibling set (build_cart, get_product, search_products) is obviously distinct from a routine recommender. It never explicitly states the verb+output ('recommends a routine'), so it is clear but not maximally crisp.

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?

Explicit when-to-use ('user asks how to start or structure jaw or face exercise') plus explicit when-NOT-to-use ('medical jaw pain, TMJ, orthodontic, or dental questions'). This is a textbook routing instruction with no inference required.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered without the description's help. The description adds scope context (catalog is limited to JAWD/face-fitness products) but says nothing about return format, result limits, or behavior on an empty match, so it clears the lower annotated bar only modestly.

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?

Two sentences with zero waste: the positive trigger is front-loaded and the exclusion follows immediately. Nothing could be cut without losing routing information.

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?

For a one-parameter, read-only catalog search with annotations covering the safety profile, the description supplies the key routing information an agent needs. It stops at 4 because no output schema exists and the description never hints at what a result looks like or how many are returned.

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% — the query parameter is already documented as 'Product name, level, or SKU'. The description adds no syntax, matching behavior, or format guidance for that field, so baseline 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?

The description names the resource domain concretely (jawline exerciser, jaw trainer, chewing/face-fitness device, JAWD products) so an agent knows what this retrieves, even though the verb 'search' is never stated explicitly. It does not distinguish itself from siblings like get_product or recommend_routine, so it stops short of a 5.

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?

It gives an explicit when-to-use trigger (user shopping for face-fitness devices or wants JAWD products) and a clear when-not-to-use exclusion list (medical jaw pain, TMJ, orthodontic, dental questions). What's missing is routing to named alternatives — an agent is told to avoid medical questions but not which sibling to use instead.

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