Jewelry by Sweet Pea Products

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explains what the tool returns, that results are filtered by metal family, width, and price sensibility, and subtly indicates the returned data should be checked via get_product for current price and stock. This is meaningful behavioral context beyond the schema.

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 filler. The first sentence delivers the core purpose and scope immediately, and the second sentence provides actionable next-step guidance. Every phrase 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?

No output schema exists, so the description must convey the nature of the return value; it does so by listing the categories of results (fitting chains, same school/team, same collection) and pointing to get_product for live price/stock. This is sufficient for an agent to understand what the tool produces and how to use the results, though it omits details like response shape and edge cases.

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 is well-documented with an example. The description reinforces that the SKU should be a pendant or charm SKU, but adds little beyond what the schema already provides. 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 uses a specific verb ('returns') and clearly identifies both the input (a pendant or charm SKU) and the output (compatible chains plus recommended paired pieces). It distinguishes itself from sibling tools like get_product and search_products by focusing on compatibility and store pairings.

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 makes clear when to use the tool: for a pendant or charm SKU when looking for compatible chains or paired pieces. It also gives explicit follow-up guidance to use get_product on returned names/URLs for live price and stock, though it does not explicitly state when to prefer a sibling tool.

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 bears full responsibility. It indicates 'live details' but does not disclose failure behavior (e.g., returns null if SKU not found), authentication requirements, or rate limits. The description is minimal.

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 a single, front-loaded sentence that clearly states the purpose and scope. Every word is necessary, with no 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?

For a simple one-parameter lookup tool with no output schema, the description covers the main fields returned. It lacks mention of error handling or pagination, but those are less critical. Minor gap: does not state if it returns a single object or null.

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 description adds little beyond the schema. It confirms the parameter is the SKU but does not provide additional semantic guidance (e.g., format, common prefixes). 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 uses a specific verb ('fetch') and resource ('product by SKU'), and lists key fields (price, availability, sizes/variants, URL). It distinguishes from siblings like search_products (which likely returns multiple results) and get_store_policies (unrelated).

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 implies use when you have a SKU and need live details, but does not explicitly state when not to use it or mention alternatives like search_products for queries without a SKU.

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 provided. The description lists the categories of information (shipping, returns, warranty, etc.), which is adequate for a read-only information retrieval tool. It does not contradict annotations.

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 a single sentence that efficiently lists the key policy areas and the store name. No wasted words, front-loaded with the main topics.

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 parameters and no output schema, the description adequately conveys what the tool returns. It does not specify output format (e.g., text vs. structured data), but for a simple policy listing, it is sufficient.

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?

There are no parameters, so the input schema is fully covered. With 0 parameters, baseline 4 is appropriate; the description adds no parameter info, which is unnecessary.

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 returns store policies including shipping, returns, warranty, etc. for 'Jewelry by Sweet Pea'. It distinguishes from siblings: get_product returns product details, search_products searches products, while this tool provides store policies.

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 implies usage context: when store policy details are needed. It does not explicitly state when not to use or name alternatives, but the sibling tools are sufficiently different, making the use case clear.

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 disclosure burden. It explains that the tool lists sibling variants, that each variant has its own SKU and URL, and that the result is empty for single-size pieces. This provides useful behavioral detail beyond the tool name.

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 no filler. It front-loads the tool's purpose and product scope, then adds the important empty-case caveat. 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?

For a one-parameter lookup tool with no output schema, the description adequately explains what the tool returns (variant SKUs and URLs), what inputs are valid, and what happens when no variants exist. Nothing essential is missing for correct 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?

The schema already documents the single 'sku' parameter with examples and 100% coverage, so a baseline score of 3 is appropriate. The description adds no significant parameter semantics beyond what the schema provides.

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 ('lists'), a specific resource (sibling products that are the same piece in other sizes or lengths), and the input domain (ring, chain, bracelet, or anklet SKU). This clearly distinguishes the tool from generic product retrieval or search tools.

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 for when to use it: when you have a SKU for a piece that may come in ring sizes or chain lengths and need its variants. It also notes the empty-result case when a piece comes in one size, though it does not explicitly name sibling tools as alternatives.

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, but description discloses 'live' nature, return fields, and query behavior. Does not mention rate limits or auth, but as a search tool, these are less critical. No contradictions.

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 no filler. First sentence covers purpose and output; second gives guidance. Front-loaded and efficient.

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 search tool, description explains output fields (name, SKU, price, etc.) and input constraints (max_results). No output schema needed. Could mention default results or pagination, but not essential.

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 covers both parameters with descriptions. Description adds value with query style advice ('best queries are short and specific') and examples, going beyond 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?

Clearly states 'live search of 100,000+ fine-jewelry products' with specific verb and resource. Distinguishes from siblings (get_product, get_store_policies) by focusing on multi-result search.

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?

Provides query tips ('short and specific', examples) and implies use for broad searches. Could explicitly compare to get_product for single-item lookups, but sibling names offer context.

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