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

With no annotations, the description carries the burden of behavioral disclosure. 'Check whether' strongly implies a read-only, non-destructive operation, and the parameter descriptions clarify what is being compared. However, it does not state whether any data is modified, whether authentication is required, or how conflicts are determined.

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 compact and well organized: a single purpose sentence followed by a clear Args list. No redundant phrasing or unnecessary detail; every sentence earns its place.

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 simple parameter signature, the presence of an output schema, and no nested objects, the description covers the essential context. It leaves minor gaps around how conflict results are represented, but those are presumably satisfied by the output schema.

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 0%, so the description must compensate. It does so by explaining both parameters in plain language: current_ingredients are the client's active ingredients and candidate_ingredients are those in the product under consideration. This adds meaningful semantic value beyond the bare array-of-strings 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 states a precise action—'Check whether'—against a specific resource: ingredient conflicts between a candidate product and the client's current routine. This clearly separates it from sibling tools like search_products or recommend_products, which serve different functions.

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 establishes clear context: use it when evaluating a candidate product against the client's current ingredients. It does not explicitly name alternatives or state when not to use it, but the use case is obvious enough that an agent can route to it appropriately.

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 carries the full disclosure burden. It discloses the core behavior: sharing at least one active ingredient, in-stock filtering, and returning 'other' products. However, it does not explicitly state whether the operation is read-only, how results are ordered or paginated, or whether the given product is excluded beyond the word 'other.'

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 entire description is one efficient sentence that front-loads the operation and matching rule, then adds a relevant use case. There is no filler or repetition of the schema.

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 2-parameter tool with no annotations and minimal schema, the description covers the main purpose and use case but omits the in_stock_only parameter behavior. It also does not explicitly distinguish itself from recommend_products, leaving some ambiguity in edge cases. An output schema exists, but the description still needs to explain how to control the stock filter.

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 0%, so the description must compensate. It clarifies product_id via 'the given product,' but it never mentions the in_stock_only parameter, its default value, or how setting it to false changes the result. This is a significant gap because that parameter controls the core stock filter.

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 specific verb and resource: 'Find other in-stock products sharing at least one active ingredient with the given product.' It clearly differentiates this from generic search_products and recommend_products by naming the exact matching rule and the out-of-stock/different-brand use case.

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 a concrete when-to-use signal: 'useful when it's out of stock or the client wants a different brand with the same effect.' It does not explicitly name alternatives or exclusions, but the use case is clear enough to guide tool selection among the siblings.

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 of behavioral disclosure. It indicates a read operation through 'Get' and lists expected data fields, but it does not explicitly state that the operation is side-effect-free, nor does it mention error cases (e.g., unknown product id) or any authentication requirements. Partial disclosure only.

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?

A single, front-loaded sentence that states the action, the key data returned, and the required identifier. No filler or redundant content, making it efficient and easy to parse.

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 simple one-parameter read tool with no output schema, the description covers the essential return fields and the required input. It does not specify the full response structure or error behavior, but the listed fields and clear purpose are 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?

The input schema has 0% description coverage for product_id, and the description only says 'by its id', which essentially restates the parameter name. It adds no format, example, or clarification about what constitutes a valid product id, so it fails to compensate for the schema's lack of detail.

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 a specific verb ('Get') and resource ('full details ... for one product') and lists the exact fields returned (price, stock, active ingredients). The phrase 'by its id' distinguishes it from sibling search/recommendation tools, making the tool's purpose unambiguous.

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 a clear usage context: use this tool when you have a product id and need full details for that specific product. It does not explicitly mention alternatives or when not to use it, but the 'by its id' condition provides enough guidance to infer appropriate use.

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 carries the behavioral burden. It discloses two important non-obvious behaviors: only in-stock products are considered and conflicting products are flagged based on current routine ingredients. It still does not define the exact return shape or handling of no-match cases, but for a non-destructive recommendation tool it provides meaningful 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 main behavior is captured in one front-loaded sentence, followed by concise per-parameter arg descriptions. There is no marketing fluff or redundant repetition of the schema. Every line contributes necessary 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 three-parameter tool with no output schema, the description gives enough to invoke it correctly and understand the general result: recommended in-stock products with conflict flags. The main gap is the lack of an explicit return format, but the behavioral description narrows that gap considerably. It is adequate for practical selection and invocation.

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 0%, and the Args section compensates by describing each paramter in plain language. It gives a concrete skin_type example ('grasa'), shows that concerns is a list of concerns to address, and clarifies current_ingredients as active ingredients already in the client's routine. It could add allowed values or the effect of omitting current_ingredients, but it adds real semantic value beyond the schema titles.

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 specific action: find in-stock products matching skin type and concerns, then flag conflicts with the client's current routine. The 'end-to-end' framing plus the conflict-flagging step clearly differentiates it from siblings like search_products and check_ingredient_conflicts. It leaves no ambiguity about what the tool does.

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 phrase 'end-to-end recommendation' implies this tool combines product search and conflict checking, so an agent can infer when it is appropriate. However, it never explicitly says when to use this tool instead of search_products, find_alternatives, or check_ingredient_conflicts. There is no when-not-to-use guidance, so usage context is only implied.

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 behavioral disclosure burden. It does disclose the return-oriented behavior of in_stock_only and the empty-string skip semantics, and 'Search' signals a read operation. Still, it does not mention result count, pagination, or any side-effect/permission profile, leaving some behavior implicit.

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 one-sentence summary is front-loaded, followed by a compact Args block. Each line adds a specific example or behavior, so there is no filler.

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 3-parameter read-only search with an output schema present, the definition provides enough invocation detail: all params explained, defaults in schema, return shape in output schema. It only lacks sibling-selection guidance and a note on result limits, so 4.

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 0%, so the description must define the parameters, and it does. skin_type and concern include concrete example values and the explicit 'Empty to skip this filter' instruction, and in_stock_only is defined as 'only return products currently in stock'. This covers all three parameters with meaning beyond the schema's names and defaults.

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 opens with a specific action ('Search') and resource ('the store's product catalog'), and narrows scope with 'by skin type and/or skincare concern'. This makes the operation clear, but it never names sibling tools or states how it differs from them, so it stays at 4.

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 Args section tells callers exactly which filters are available and how to skip each one, but there is no explicit when-to-use or when-not-to-use guidance relative to siblings like get_product_details, find_alternatives, or recommend_products. Usage is implied rather than stated.

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