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

Without annotations, the description carries the full burden. It discloses that availability is based on community-reported store data (not real-time inventory), which is a key behavioral trait. However, it omits other aspects like response format, error handling, or authentication needs.

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 with two short paragraphs and an Args section. It is front-loaded with the core purpose. No unnecessary sentences, but the Args line is somewhat redundant with the parameter description.

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 existing output schema, the description covers the main functionality and data source limitation. It lists return elements (status, store, timestamp) but lacks explicit output schema details, which are presumably provided separately.

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 description adds 'Product barcode/EAN to check' for the single parameter, providing context beyond the schema's title. Despite 0% schema coverage, this adds meaningful semantics, though format constraints are missing.

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 verb ('Check'), resource ('product availability and pricing'), and scope. It differentiates from siblings like get_product_details and search_products by focusing on availability and pricing specifically.

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?

No explicit guidance on when to use this tool versus alternatives. It does not mention exclusions or when not to use it, leaving the agent without comparative 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?

No annotations are provided, so the description must fully convey behavioral traits. It describes the comparison action and dimensions but omits details on error handling, authentication requirements, or whether the operation is read-only. It reveals the parameter constraint (2-5 products) but does not explain the return format, which is partly mitigated by the existence of an output 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?

The description is extremely concise with no wasted words. It front-loads the core purpose, then lists key dimensions, and finally defines the argument. The structure is clear 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?

Given the existence of an output schema, the description need not detail return values. It covers the main functionality well, but could provide slightly more context about expected behavior when ids are invalid or not found. Overall, it is sufficiently complete for a simple comparison 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?

The input schema provides minimal information (array of strings) with 0% description coverage. The description compensates fully by specifying that product_ids are 'product barcodes' and constraining them to '2-5 products', adding significant meaning beyond the schema. For a single parameter, this is excellent.

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 performs 'side-by-side product comparison' and lists specific dimensions (nutrition, labels, environmental impact, ingredients). It distinguishes itself from sibling tools like get_product_details (single product) and search_products (search) by focusing on comparison.

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 comparing multiple products across dimensions, providing clear context. However, it lacks explicit when-not-to-use statements or references to alternative sibling tools for specific use cases, so it is not a perfect 5.

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 must fully disclose behavior. It states the output is a 'standardized format' but does not mention whether the operation is read-only, any side effects, or potential errors. Performance, rate limits, or state changes are not addressed.

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 extremely concise (three sentences and a bulleted Args list), front-loaded with the key purpose, and every sentence adds value. 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 that an output schema exists, the description does not need to explain return values. It covers input parameters and the conversion purpose. It is sufficient for a conversion tool, though it could mention possible errors or limitations.

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 in the schema itself, but the description compensates by explaining the feed_url as 'URL to the product feed' and format with its possible values and default, adding meaning beyond the bare schema properties.

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: converting a product feed URL into a normalized schema. It specifies the verb 'convert', the resource 'product feed URL', and the outcome 'agent-friendly normalized schema', and distinguishes from siblings like generate_product_schema by focusing on external feeds.

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 supported formats (JSON, CSV, Open Food Facts) but does not explicitly state when to use this tool versus alternatives like generate_product_schema. The guidance is implicit, lacking when-not-to-use or exclusion criteria.

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 exist, so the description carries full burden. It discloses that the tool transforms raw data into a structured schema and lists output fields, implying no side effects. It is transparent enough for a simple generator.

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 with a clear one-liner, explanation, bullet list, and parameter detail. It is not overly verbose, though the bullet list could be slightly trimmed.

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 only one parameter, no annotations, and an output schema (referenced), the description fully explains the tool's operation, input requirements, and output structure, making it 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 coverage is 0%, but the description compensates by detailing the expected fields in 'product_data' (name, price, description, category). This adds meaningful context 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 generates a standardized agent-readable product listing from raw data, specifying the verb and resource. It implicitly distinguishes from siblings like 'check_availability' and 'search_products' by focusing on schema generation.

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 explains the tool's purpose but does not explicitly state when to use it versus alternatives or provide exclusions. It implies usage for creating machine-readable product schemas but lacks direct guidance.

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. Description implies read-only ('Get full structured product data') but does not explicitly state safety, idempotency, or side effects. Adequate but not thorough.

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?

Very concise: one-line purpose, return fields list, and parameter doc. No fluff, 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?

Has output schema and lists key return fields (nutrition, ingredients, allergens, etc.). Single parameter, low complexity. Missing guidance on when to use among siblings, but 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?

Schema has 0% description coverage; description adds format (barcode/EAN) and an example, significantly clarifying the parameter beyond the schema's minimal 'Product Id'.

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 it retrieves full structured product data by barcode, specifying the resource (product) and operation (get). Example given. Distinct from siblings like search_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?

Implied usage when you have a product barcode, but no explicit when-to-use, when-not-to-use, or comparison with sibling tools like check_availability or search_products.

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 full responsibility. It discloses the max_price limitation and mentions normalized output structure, but omits rate limits, pagination details, or side effects. Adequate but not exhaustive.

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 (two short paragraphs) and well-structured: a summary sentence followed by a clear, bullet-like Args list. No redundant 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 no annotations and the presence of an output schema (not shown), the description adequately covers input parameters and mentions the data source limitation. It lacks details on authentication, error handling, or pagination beyond limit, but is largely sufficient for a search 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?

The schema has 0% description coverage, but the tool description's Args section compensates fully by explaining each parameter with examples, defaults, and constraints (e.g., limit max 50, max_price not available for OFF). This adds significant meaning.

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 'Search products with agent-friendly structured results.' and enumerates the returned fields, making the purpose specific and distinct from siblings like get_product_details 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 provides search examples ('organic chocolate') and explains each parameter's role, including limitations ('not available for OFF data'). While it doesn't explicitly contrast with siblings, the context implies this is for broad keyword-based searches.

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