product-intelligence

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

Annotations already indicate readOnly and idempotent, and the description adds detailed behavioral context: what it evaluates (7 ecosystems, 8 protocols, hub requirements, subscription costs) and its output (score 0-100, verdict categories). There is 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?

The description is brief (two sentences plus a note and link), front-loaded with the core action, and every sentence contributes essential information without 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?

The description explains the output (score and verdict) and evaluation criteria, which is sufficient given no output schema. It mentions a methodology link for further detail, but could be slightly more comprehensive on edge cases 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?

The input schema already covers all parameters with descriptions (100% coverage). The overall description does not add significant additional meaning beyond what the schema provides for individual parameters.

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 tool's function: checking compatibility of a smart home device with a user's existing setup using a proprietary engine. It lists the ecosystems, protocols, and outputs, and distinguishes itself from sibling tools by highlighting its unique cross-ecosystem analysis.

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 when to use the tool (to evaluate compatibility) and mentions its uniqueness, but does not explicitly state when not to use it or provide direct comparisons with sibling tools like compare_products or get_product_verdict.

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, idempotentHint=true, and destructiveHint=false, indicating safe, read-only behavior. The description adds context about using editorially curated data, returning data-backed winner determination, and providing a methodology link. This goes beyond annotations without contradicting 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 concise (three sentences) and front-loaded with the core action ('Side-by-side comparison of 2-4 smart home products'). Every sentence adds value: methodology mention, comparison dimensions, and resulting output. No unnecessary words or repetition.

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 single parameter and no output schema, the description provides sufficient context: what the tool does, data source, comparison criteria, and return details. It does not cover edge cases (e.g., invalid product names), but for a comparison tool with safe annotations, it is largely 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?

Schema description coverage is 100% for the single parameter (products), including an example. The tool description does not repeat parameter details but adds context about how the comparison is conducted. Since the schema already sufficiently documents the parameter, a baseline score of 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 states it performs a side-by-side comparison of 2-4 smart home products using editorially curated data, listing specific comparison dimensions (SHE Consensus Score, ecosystem support, subscription costs, key differentiators) and output (winner determination with URLs). It distinguishes from sibling tools by focusing on comparison rather than single-product verdicts or buying guides.

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 the tool is for comparing multiple smart home products but provides no explicit guidance on when to use it versus alternatives like check_compatibility or get_buying_guide. No when-not-to-use cases or exclusion criteria are mentioned, leaving the agent to infer appropriate usage from 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 and idempotentHint=true. The description adds behavioral context: guides are authored by Nicholas Miles, updated regularly, and include a methodology link. There is 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?

The description is concise yet informative, with every sentence contributing useful details (source, author, content, methodology, updates). It is well-structured and front-loaded with the key action.

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 read-only tool with no output schema and only 2 parameters, the description covers all necessary aspects: what is returned (title, top picks, URL, analysis), methodology, and data freshness. It is complete for decision-making.

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 both parameters described. The description adds value by providing examples for topic ('best smart thermostat for renters') and explaining category as an optional filter. This enhances understanding 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 retrieves an editorially written buying guide from a specific source (SmartHomeExplorer) with details on author, content, and methodology. It distinguishes itself from sibling tools (check_compatibility, compare_products, get_product_verdict, search_smart_home_products) by focusing on a curated guide with top picks.

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 getting a comprehensive buying guide with expert analysis and top picks. It does not explicitly state when to use it versus alternatives, but the detail on what it returns (title, top 3 picks, URL) provides context that helps with selection.

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, idempotentHint, and destructiveHint. The description adds context by explaining the scoring methodology, source list, and return fields, providing value beyond the annotations without contradicting 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 with no wasted words. The first sentence states the core purpose, the second elaborates on outputs and sources. Information 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?

Despite no output schema, the description lists all return fields (score, verdict, price range, pros/cons, URL with quotes) and references the methodology. For a read-only, single-parameter tool, this provides sufficient context for an agent to understand inputs and outputs.

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% for the single parameter product_name, which already provides an example and format. The description adds no additional parameter information, so it meets the baseline but does not exceed it.

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', the resource 'SHE Consensus Score and expert verdict', and the domain 'smart home product'. It distinguishes itself from sibling tools by focusing on aggregated expert reviews 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 use when needing a consensus score for a specific product but lacks explicit guidance on when to use this tool versus alternatives like search_smart_home_products or compare_products. No exclusions or context for decision-making.

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 destructiveHint=false, and the description adds valuable behavioral details: data is updated weekly, scores come from 12 named expert publications with published methodology, and returns source-linked URLs. There is 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?

The description is a single paragraph of 4-5 sentences with no wasted text. It front-loads the main purpose, then efficiently covers data source, filtering, update frequency, and return format. Every sentence 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 7 parameters and no output schema, the description adequately covers what the tool does and returns. It mentions the score methodology and source URLs, which compensates for the lack of output schema. Minor omission: the max_results default behavior is only in the schema, but overall it is 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 input schema covers all 7 parameters with descriptions (100% coverage), so the description's mention of filtering by ecosystem, subscription, price, and category adds modest semantic value beyond the schema. A score of 3 reflects that the schema already 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 searches an editorially curated database of 1,080+ smart home products, returning scored recommendations. The verb 'Search' and resource 'SmartHomeExplorer's database' are specific, and the tool distinguishes itself from siblings (e.g., check_compatibility, compare_products) by focusing on product discovery.

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 on when to use the tool (for searching products with various filters) but does not explicitly state when not to use it or mention alternatives. However, the presence of sibling tools implies differentiation, and the description is otherwise clear about its purpose.

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