iDevice Wearables

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

Annotations already declare read-only, so no contradiction. The description adds behavioral details: uses live specs, handles unreleased products, returns ordered array. This enriches agent understanding without overlapping 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?

Three sentences, front-loaded with purpose, followed by distinctive feature and instructions. No redundant or vague language; every sentence contributes essential 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?

Given the tool has an output schema (not shown), the description adequately covers inputs, distinguishing features, and return format. It references a sibling for slug retrieval, but could briefly mention that comparisons are live and comprehensive. Missing explicit error handling or prerequisites.

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 descriptions for both slugs. The description adds value by clarifying that slugs come from list_roadmaps, that both can be unreleased, and that return order matches input order. These details go beyond 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 compares two wearable products side by side, specifying the resources used (live specs from per-category spec tables). It distinguishes from siblings like get_product_specs by emphasizing comparison and mentioning unreleased products.

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 context on when to use (comparing two products, including unreleased) and references list_roadmaps for obtaining slugs, but does not explicitly exclude alternative tools like get_product_specs for single-product queries.

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 and openWorldHint=false. The description adds value by listing the specific outputs (model, price, chip, recommendation, cycle position, etc.) and confirms the input is a slug. It does not contradict annotations and provides useful behavioral context beyond the structured fields.

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 first sentence concisely states purpose and output; the second provides a clear usage example and cross-reference. Every word 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 simple tool with one parameter and an output schema, the description is complete. It covers what the tool does, what it returns, what inputs to use, and where to find valid slugs. No gaps given the complexity.

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% and the schema description already provides examples. The description adds the reference to list_roadmaps for finding slugs, which is helpful but only marginal extra value. Baseline is 3 because schema does the heavy lifting.

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 retrieves a buy-now-or-wait recommendation for a wearable product by slug, differentiating it from siblings like get_product_specs or get_roadmap. The verb 'get' and resource 'buyers guide' are specific and 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 usage example ('Use a product slug like 'apple-watch'...') and references list_roadmaps for finding slugs, but does not explicitly state when to use this tool versus alternatives like compare_products or get_product_specs. It implies context but lacks explicit exclusions or 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?

Annotations provide readOnlyHint=true, and description adds that results are tagged with confidence tiers and source citations, enriching behavioral context without contradicting 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?

Two concise sentences with front-loaded purpose. Every sentence adds value: first defines action, second details output structure. 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?

With output schema present, description appropriately summarizes return values (confidence tiers and citations). Covers key aspects for a two-parameter, mainly scalar-output tool.

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 (100% coverage). Description adds semantic nuance by explaining the display names for confidence tiers (e.g., 'unofficially-confirmed [shown as "Widely reported"]') 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?

Description clearly states 'Get reported features and specs for a wearable product', specifying verb and resource, and lists confidence tiers. Distinguishes from sibling tools like get_product_specs and get_news by its focus on claims with confidence levels.

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 implies usage for retrieving claims with confidence tiers but does not explicitly compare to or exclude alternatives like get_product_specs. No when-not-to-use guidance is given.

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 indicate read-only (readOnlyHint=true). The description adds that results have a 'type' field, which is useful. No contradictions or missing behavioral traits.

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, front-loaded with purpose and examples, no extraneous words. Every sentence provides value.

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 output schema exists and the tool is simple (0 required params, 2 optional), the description covers all necessary functionality, examples, and filtering.

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%, but the description adds context with examples for each parameter and explains filterability, going beyond the schema definitions.

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 gets definitions for wearable technology terms and product codenames, provides specific examples (PPG, SpO2, N50, Aperol), and distinguishes result types. No sibling tool has a similar purpose.

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 for term/codename lookups and filterability, but lacks explicit guidance on when to use this tool vs. alternatives or when not to use it.

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?

The description is consistent with the readOnlyHint annotation, stating it 'returns laws'. It does not detail pagination or rate limits, but the presence of an output schema reduces the need for additional return value explanation.

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 only two sentences, front-loaded with the main purpose in the first sentence. No unnecessary words 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 readOnly annotation and output schema, the description covers the tool's function, scope, and filter options. It lacks explicit mention of default behavior (e.g., returns all if no filters applied) but is otherwise complete.

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 provides complete descriptions for all four parameters (100% coverage). The tool description only repeats the filterable fields without adding deeper semantic context or examples.

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 specifies the verb 'Get' and the resource 'wearable tech regulations and legislation tracked by iDevice', along with specific product examples and statuses. This distinguishes it from siblings like get_claims or get_news.

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 for retrieving regulatory data filtered by status, category, product, or jurisdiction, but does not explicitly state when to use this tool over alternatives such as get_claims or get_news.

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 and openWorldHint=false, so description is consistent. It adds context that results include editorial commentary and optional category filtering, which is beyond annotations. This enriches the agent's understanding of what to expect.

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 concise sentences, front-loaded with key purpose, and no extraneous information. Every word adds value.

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 has output schema, annotations, and low parameter count, the description provides sufficient context: it explains return structure (story, summary, editorial take) and filtering option, making it complete for an agent to select and use 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 baseline is 3. The description reiterates optional filtering by category but doesn't add significant meaning beyond schema descriptions. No credit for repeating schema info.

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 it retrieves wearable tech news clusters with editorial commentary, specifying each result has a one-sentence summary and multi-sentence editorial take, and mentions optional filtering by product category, distinguishing it from siblings like search_articles or get_timeline.

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 implicitly indicates use for getting latest news with editorial commentary, but does not explicitly state when to prefer this over siblings or provide when-not-to-use guidance. Sibling names suggest alternatives but no direct exclusions.

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 indicate read-only and open-world behavior. The description adds an important behavioral detail: the inclusion of an affiliate link in the response. No contradictions found.

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: first states purpose and output, second links to a sibling tool and mentions the affiliate link. Every sentence adds value; it is 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?

Given the presence of annotations (readOnlyHint, openWorldHint) and an output schema, the description covers purpose, usage guidance, return data specifics, and affiliate link. No gaps for this simple tool.

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% for the single 'subject' parameter, and the schema description provides examples. The tool description does not add new meaning beyond the schema; it repeats the schema's example. Baseline 3 applies.

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 retrieves Amazon price history for tracked wearable products over the past 90 days, listing specific return data (current price, list price, low/high, daily points). It distinguishes from sibling tools like compare_products and get_product_specs by focusing solely on price history.

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 instructs users to use list_tracked_products to find valid subjects, providing a clear prerequisite. It does not explicitly state when not to use this tool, but for a simple data retrieval tool, that is acceptable.

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 indicate read-only and closed-world. Description adds detail about sourcing from live spec tables, coverage of current/upcoming/shipping products, and categories listed, providing useful context beyond 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?

Two sentences with no extraneous content. First sentence states action and scope, second provides examples and reference to another tool. Highly 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?

Given output schema existence, description adequately covers input, coverage, and source. Lacks mention of error handling for invalid slugs, but overall sufficient for tool usage.

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 slug with 100% coverage. Description adds value by giving concrete examples and directing to list_roadmaps for available slugs, enhancing understanding of valid inputs.

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 it retrieves detailed tech specs for wearable products by slug, lists specific categories (display, chip, battery, etc.), and distinguishes from siblings like compare_products or get_claims.

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 usage context with examples and links to list_roadmaps for valid slugs, but does not explicitly state when not to use this tool or mention alternatives like compare_products for comparisons.

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 indicate readOnlyHint=true. The description adds behavioral detail by listing the full set of data returned (FAQ, features with confidence tiers, leak timeline, etc.), which goes beyond the annotation value. No contradiction with 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?

Two sentences: first succinctly lists return content, second gives usage instruction. Every sentence is informative and necessary; 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 the presence of an output schema (handling return format) and annotations (read-only), the description covers what the tool returns and how to use it. The instruction to use list_roadmaps first addresses a common prerequisite. Completely adequate for a single-parameter read-only tool.

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?

Only one parameter 'slug' with schema coverage 100% so baseline is 3. The description provides example values ('apple-glasses', 'apple-ring', 'pixel-watch-5'), adding concrete semantic context beyond the schema's generic 'Product slug' description.

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 retrieves 'full roadmap' for a wearable product and enumerates specific components (expected window, price, confidence, etc.). It explicitly distinguishes from sibling 'list_roadmaps', which is for finding the correct slug.

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 directly tells the agent to 'Use list_roadmaps first to find the correct slug', providing clear when-to-use guidance and suggesting an alternative tool for prerequisite action.

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?

The description adds value beyond annotations by specifying the types of events included and the structure of each entry (date, description, source citation). Annotations already indicate read-only and closed-world, and the description does not contradict them.

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 primary action and scope, and contains no redundant words or filler. Every sentence provides essential 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?

Given a single parameter, read-only annotations, and an existing output schema, the description provides a sufficient overview. It clearly communicates the tool's purpose and output structure, though it could explicitly mention the slug identifies the product.

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 covers the sole parameter 'slug' with 100% description coverage. The description does not add additional semantic details about the parameter, so it meets the baseline without enhancement.

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 retrieves a chronological history of specific event types (leaks, patents, announcements, rumors) for a wearable product, using a specific verb and resource. It distinguishes itself from sibling tools like get_news or get_roadmap by focusing on a combined timeline of multiple event categories.

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 for historical timeline queries but does not explicitly state when to use this tool versus alternatives like get_news or get_product_specs. No when-not or alternative tool mentions are provided.

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. The description adds return field details and filtering options, enhancing transparency beyond annotations. No contradiction.

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 concise sentences covering purpose, return content, and optional filters. Front-loaded with essential information, 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?

With an output schema present, the description adequately covers what the tool does, what it returns, and optional filtering. No missing critical information for a list tool.

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 clear descriptions for both parameters. The description only restates the filtering capability without adding new meaning beyond 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 verb 'list' and the resource 'tracked wearable product roadmaps', specifies return fields (slug, model name, etc.), and distinguishes from sibling 'get_roadmap'.

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 mentions optional filters (category, expected year) but does not explicitly state when to use this tool over alternatives. However, the context of listing vs. individual retrieval is 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?

Annotations already declare readOnlyHint true, so the description doesn't need to restate it. It adds value by listing returned fields but doesn't disclose any behavior beyond that (e.g., pagination, rate limits).

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 that front-load the purpose and efficiently convey key details without waste. 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 no parameters and an existing output schema, the description is fairly complete. It explains the output fields and a follow-up use. However, it could mention if there is any filtering or scope limitation (e.g., 'all' implies no 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?

No parameters exist in the schema, and schema coverage is 100%, so the description need not add param info. Baseline for zero parameters is 4. The description correctly focuses on what the tool returns.

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?

Description clearly states the tool lists tracked wearable products for Amazon pricing on iDevice, and specifies the returned fields (subject name, ASIN, title, category). It distinguishes itself from siblings like get_price_history or compare_products by being the only list tool for tracked items.

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 a follow-up suggestion ('Use subject names with get_price_history') but lacks explicit guidance on when to use this tool versus alternatives like get_product_specs or compare_products. No when-not-to-use conditions are mentioned.

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 and openWorldHint=false, so the safety profile is clear. The description adds context about the archive's focus (wearable tech, specific brands) but does not disclose additional behavioral traits like pagination, sorting, or result structure. It provides moderate added value beyond 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, well-structured sentence that efficiently conveys the tool's purpose and scope. Every part adds value without redundancy or 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?

Given the existence of an output schema (reducing need to explain return values) and full schema coverage, the description is mostly complete. It could be enhanced by mentioning default ordering or result format, but current content is sufficient for an effective agent interaction.

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 schema already documents all parameters. The tool description does not add any extra semantic meaning beyond what the schema provides (e.g., no explanation of how 'category' filter works). 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 clearly identifies the tool as a search over iDevice's editorial archive of wearable tech news. It specifies the domain (smart glasses, rings, watches, etc.) and lists key brands (Apple, Meta, etc.), which helps distinguish it from sibling tools like 'get_news' or 'compare_products'.

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 (searching editorial articles about wearables) but does not explicitly state when to use this tool versus alternatives. It lacks guidance on when not to use it (e.g., for non-wearable news) and does not mention sibling tool comparisons.

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 annotations already providing readOnlyHint and openWorldHint, the description adds useful context: it retrieves data from ClinicalTrials.gov, returns specific fields, and filters for wearable devices. No contradictions with 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 two sentences, front-loaded with the action and return values, and every word adds value. 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?

Given the presence of an output schema and rich annotations, the description is complete: it specifies the source, scope, and return fields, and the utility statement provides sufficient 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 does not add significant meaning beyond what the parameter descriptions already provide; it only reinforces the 'device' focus.

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 'Search' and clearly identifies the resource 'ClinicalTrials.gov' and the focus on 'peer-reviewed clinical studies involving a wearable device'. It lists the return fields, effectively distinguishing itself from sibling tools like get_news or search_articles.

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 states it's 'useful for understanding the health research evidence behind a device', providing clear context for when to use it. However, it does not explicitly mention when not to use it or list alternatives among siblings.

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