product-intelligence

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

The description adds valuable behavioral context beyond annotations. While annotations indicate read-only, non-destructive, and idempotent operations, the description details the tool's methodology (evaluates 7 ecosystems, 8 wireless protocols, etc.), output format (score 0-100 and verdict), and uniqueness claim. No contradictions with annotations exist.

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 well-structured and front-loaded with the core purpose, followed by details on evaluation scope, output, and uniqueness. Every sentence adds value, such as specifying ecosystems, protocols, and methodology, with no wasted 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 tool's complexity and lack of output schema, the description is complete enough. It explains what the tool does, its unique value, evaluation criteria, and output format (score and verdict). With annotations covering safety and idempotency, and schema covering parameters, the description fills in necessary behavioral and contextual gaps effectively.

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 thoroughly. The description does not add specific meaning or syntax details for parameters beyond what the schema provides, such as explaining how 'existing_devices' or 'candidate_product' are processed. Baseline 3 is appropriate given high schema coverage.

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 purpose: 'Check if a smart home device fits a user's existing setup using SmartHomeExplorer's proprietary Compatibility Engine.' It specifies the verb ('check') and resource ('smart home device'), and distinguishes itself from siblings by emphasizing its unique cross-ecosystem analysis that no other public service offers.

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 context for when to use this tool: for evaluating device compatibility across ecosystems. However, it does not explicitly mention when not to use it or name specific alternatives among the sibling tools (e.g., compare_products, get_product_verdict), leaving some room for improvement in sibling differentiation.

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, non-destructive, and idempotent behavior, which the description doesn't contradict. The description adds valuable context beyond annotations: it discloses the data source ('editorially curated data'), methodology reference ('smarthomeexplorer.com/she-score-methodology'), and output specifics ('data-backed winner determination with source-linked review page URLs'), enhancing transparency about the tool's reliability and return 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?

The description is efficiently structured in two sentences: the first outlines the tool's purpose, scope, and comparison metrics, while the second covers output and methodology. Every sentence adds value without redundancy, and it's front-loaded with key information, making it easy to parse quickly.

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 moderate complexity (comparing multiple products with curated data), rich annotations (read-only, idempotent, etc.), and no output schema, the description is largely complete. It explains what the tool does, data sources, output format, and methodology. However, it could be more explicit about error handling or limitations (e.g., product availability), slightly reducing completeness.

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%, with the 'products' parameter fully documented in the schema (array of 2-4 strings, example provided). The description doesn't add further parameter details beyond what the schema already specifies, such as format requirements or validation rules. With high schema coverage, a baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.

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 specific action ('Side-by-side comparison'), resource ('2-4 smart home products'), data source ('SmartHomeExplorer's editorially curated data'), and comparison metrics (SHE Consensus Score, ecosystem support, subscription costs, key differentiators). It distinguishes from siblings by focusing on direct product-to-product comparison rather than compatibility checking, buying guides, verdicts, or general searches.

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 defines when to use this tool by specifying it compares '2-4 smart home products' and lists the comparison metrics, suggesting it's for detailed product evaluation. However, it doesn't explicitly state when to choose this over alternatives like 'get_product_verdict' or 'search_smart_home_products', nor does it mention prerequisites or exclusions beyond the product count range.

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 indicate read-only, non-destructive, and idempotent behavior, which the description aligns with by describing a retrieval operation. The description adds valuable context beyond annotations: it mentions the author (Nicholas Miles), methodology details, regular updates, and the specific content returned (e.g., top 3 product picks, guide URL with analysis), enhancing transparency about what the tool provides.

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 appropriately sized and front-loaded, starting with the core purpose. However, it includes some extraneous details like the author's name and methodology URL, which, while informative, could be streamlined. Overall, it's efficient but has minor room for improvement in focus.

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 complexity (retrieval with filtering), rich annotations (e.g., read-only, idempotent), and no output schema, the description is mostly complete. It details what is returned (guide title, top picks, URL) and update frequency, but lacks explicit error handling or pagination info. With annotations covering safety, it provides adequate context for use.

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 parameters are well-documented in the schema. The description does not add significant semantic details beyond the schema, such as explaining parameter interactions or usage examples. It mentions 'topic' indirectly but doesn't elaborate on parameter semantics, meeting the baseline for high schema coverage.

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 purpose: 'Get an editorially written buying guide from SmartHomeExplorer's library of 170+ guides.' It specifies the resource (buying guides), the source (SmartHomeExplorer's library), and distinguishes it from siblings by focusing on editorial guides rather than compatibility checks, product comparisons, verdicts, or product searches.

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 when a user needs a buying guide with expert analysis and top product picks, but it does not explicitly state when to use this tool versus alternatives like 'compare_products' or 'get_product_verdict'. No exclusions or specific contexts for tool selection 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 cover safety (readOnlyHint, non-destructive) and idempotency, but the description adds valuable context: it explains the proprietary SHE Consensus Score methodology, lists the 12 expert sources, and details the return fields (score, verdict, price range, pros/cons, URL). This enhances understanding beyond the 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 efficiently structured in two sentences: the first states the purpose and methodology, and the second lists return fields. Every sentence adds essential information without redundancy, making it front-loaded and concise.

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 complexity (proprietary scoring, multiple return fields) and lack of output schema, the description provides a comprehensive overview of what is returned. However, it could improve by specifying error cases or limitations, such as product availability or data freshness.

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 fully documents the single parameter 'product_name'. The description does not add extra parameter details beyond what the schema provides, such as format examples or constraints, meeting the baseline for high schema coverage.

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 purpose with specific verbs ('Get the SHE Consensus Score and expert verdict') and resources ('for a specific smart home product'), distinguishing it from siblings like 'compare_products' or 'search_smart_home_products' by focusing on detailed expert consensus for a single product.

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 expert consensus on a specific product but does not explicitly state when to use this tool versus alternatives like 'get_buying_guide' or 'compare_products'. No exclusions or prerequisites are mentioned, leaving some ambiguity in context.

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, idempotentHint=true, and openWorldHint=false. The description adds valuable context beyond annotations: it specifies the data source (editorially curated database), methodology transparency (published methodology link), data volume (1,080+ products), update frequency (weekly), and return format (scored recommendations with source-linked URLs). 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 efficiently structured in two sentences: first establishes purpose and data quality, second explains filtering and returns. Every element (database size, score methodology, filter dimensions, update frequency, return format) serves a clear informational purpose with zero 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?

For a search tool with rich annotations (read-only, idempotent, closed-world) and full schema coverage, the description provides excellent context about data provenance, quality metrics, and update cadence. The main gap is lack of output schema, but the description partially compensates by specifying return format (scored recommendations with URLs). Could be more explicit about pagination/limits given max_results parameter.

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 fully documents all 7 parameters. The description adds marginal value by mentioning filterable dimensions (ecosystem compatibility, subscription requirements, price, category) that align with parameters, but doesn't provide additional syntax or format details beyond what the schema already specifies.

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 searches a curated database of smart home products with specific metrics (SHE Consensus Score from 12 expert publications). It distinguishes from siblings by focusing on search/filtering rather than compatibility checks, comparisons, buying guides, or verdicts.

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 finding products with expert scores and filtering capabilities, but doesn't explicitly state when to use this versus alternatives like compare_products or get_buying_guide. It provides context about data freshness (updated weekly) but lacks explicit guidance on tool selection.

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