grocery

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

The description mentions data sources but does not disclose behavioral traits such as read-only nature, data freshness, pagination, or rate limits. The annotation 'readOnlyHint' is not present, so the description should explicitly indicate the tool's non-destructive behavior.

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, consisting of two sentences that clearly state the tool's purpose and data source. Every sentence adds value 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 does not explain return values or output format, and it omits details about how parameters like weeks or market affect results. Given no output schema, the description should provide more context on expected data shape.

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 50% of parameters with descriptions (weeks and commodity). The tool description lists categories matching the category enum but adds no additional meaning for market or other parameters. A description that elaborates on market and category options would improve this score.

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 commodity prices for agricultural products and lists specific categories. However, it does not distinguish itself from sibling tools like 'food_retail_prices' or 'food_price_compare', leaving ambiguity about when to use this tool.

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 guidance is provided on when to use this tool versus alternatives. The description lacks any mention of appropriate use cases or 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?

With no annotations provided, the description must disclose behavioral traits. It states 'Shows per-serving and per-100g when available,' revealing output format behavior. It does not cover error handling or authentication, but the core behavioral context is well-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 two concise sentences, front-loaded with the core purpose, and every word adds value. No redundancy or fluff.

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 output schema and no annotations, the description covers purpose, usage guidance, parameter defaults, and output format (per-serving and per-100g). It could mention cross-category comparison or units, but overall it is sufficiently complete for a 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?

Schema coverage is 100%, so both parameters are documented. The description adds value by clarifying the default behavior for the 'nutrients' parameter ('Specific nutrients to compare (default: all major)'), exceeding what the schema alone 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 uses a specific verb ('Compare') and resource ('nutrition facts between 2-5 food products'), clearly distinguishing from siblings like food_nutrition (single product) and food_price_compare (price focus). It also provides a common use case ('which is healthier').

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 phrase 'Great for "which is healthier" questions' provides clear context for when to use the tool. However, it does not explicitly state when not to use it or mention alternatives, though the context of sibling tools implies 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?

No annotations are provided, so the description must fully disclose behavior. It indicates a read-only operation ('Show') but does not explicitly confirm no side effects, no authentication requirements, or any additional behavioral traits. With no annotation safety net, the coverage 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?

Two sentences, front-loaded with action and resource, no extraneous words. 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 zero parameters and no output schema, the description adequately covers the tool's return content. However, it could mention whether results are paginated or if there are any limits, but for a simple listing tool this 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?

The input schema has zero parameters, so the description's job is to explain what the tool returns. It does so thoroughly, listing the fields shown. Baseline of 4 is appropriate given no 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 uses specific verb 'Show' and clearly identifies the resource ('all data sources powering Food Intel'). It enumerates the details returned (record counts, update times, origin URLs, tool existence), making it distinct from sibling tools like food_search or food_nutrition.

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 this tool is for transparency and auditing ('Full transparency'), but it does not explicitly state when to use it over alternatives, nor does it provide when-not 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 are provided, so the description carries the full burden. It mentions combining real-time pricing, nutrition, allergens, and stock, indicating a read operation. However, it does not disclose authentication needs, rate limits, or whether results are limited to Kroger. The behavioral context is adequate but not comprehensive.

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: the first states the general purpose, the second provides a bolded, memorable example. Every sentence adds value, and the structure is front-loaded.

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 13 parameters and no output schema, the description provides a solid context with the example query, but lacks details on return format or how results are structured. It sufficiently covers input context but leaves output behavior unspecified.

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 85%, so most parameters are described in the schema. The description adds value by demonstrating how parameters combine in a realistic example (SNAP-eligible, gluten-free, sub-$4, protein per dollar). This enriches understanding beyond the individual parameter descriptions.

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 finds products matching dietary, nutritional, price, and availability criteria, and provides a specific complex query example. It distinguishes itself from siblings like food_search and food_nutrition by emphasizing multi-criteria filtering.

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 complex combined queries with 'THE query Google cannot answer' but does not explicitly state when to use this tool versus alternatives like food_search or food_nutrition. No when-not-to-use or exclusion criteria are 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?

No annotations are provided, so the description carries full burden. It discloses that the tool performs intersection filtering ('ALL constraints simultaneously'), which is useful. However, it does not mention behavior when no products satisfy all constraints, potential performance issues, or data freshness.

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, followed by a concrete example. No extraneous words, 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 no output schema, the description could hint at return format (e.g., 'returns list of products with details'). For a tool with 4 parameters including a nested array, it is reasonably complete but lacks explanation of output structure.

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 75% (3 of 4 parameters have descriptions). The description adds no additional parameter semantics beyond what the schema already provides, but the example helps contextualize how constraints are combined. Baseline score 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 the tool finds food products satisfying all dietary constraints of multiple household members simultaneously. The example with specific dietary needs (low-fat, high-protein) illustrates the purpose distinctively from sibling tools like 'food_diet_filter' and 'food_meal_recommend'.

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 to find products for a household with differing dietary needs, and the example provides concrete context. However, it does not explicitly state when not to use this tool or mention alternatives like 'food_diet_filter' for single-person filtering.

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 burden. It discloses that the tool shows every ingredient with data source origin, which adds behavioral context. However, it does not mention any destructive actions, auth requirements, or rate limits. Given no annotations, this is adequate but not exceptional.

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 the core action, 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?

The description covers the main functionality and output (ingredients, allergens, data source). However, it does not explain the return format, pagination, or behavior when no results are found. Given no output schema, more context would help, but it is minimally adequate.

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 67% (descriptions for query and allergen, not for limit). The description does not add semantic detail beyond the schema; it does not explain the limit parameter or provide examples. With moderate schema coverage, the description should compensate but does not, so score is baseline 3.

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 'Look up' and the resource 'ingredient lists and allergens for branded food products'. It distinguishes from siblings like food_nutrition and food_diet_filter by focusing on ingredient details and allergen filtering.

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 use cases ('dietary restrictions, allergies, MAHA transparency') but does not explicitly state when not to use this tool or mention alternatives. It provides usage context but lacks 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 are provided, so the description must convey behavioral traits. It lists return attributes but does not disclose whether authentication is needed, rate limits, side effects (none expected), or error behavior. The tool is read-only by implication ('Get'), but no explicit statement about safety or constraints.

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 front-loads the main purpose and then lists specific features. It is efficient with no wasted words, though the list of attributes is long. Slightly more structured formatting (e.g., bullet points) could improve readability, but it remains clear 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 no output schema, the description compensates by listing many return attributes, providing reasonable context for what the tool returns. However, it lacks guidance on how to use parameters (e.g., priority between upc, query, kroger_id) and does not mention pagination, error handling, or result limits. Completeness is adequate for a simple lookup tool but could be richer.

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 75% (3 of 4 parameters described). The description adds value by listing the detailed return fields, which helps understand what each parameter influences. However, it does not add direct meaning or usage constraints beyond what the schema already provides for individual parameters. 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?

Description clearly states 'Get Kroger-specific product details' and lists numerous specific attributes (pricing, aisle/shelf location, stock level, etc.). This distinguishes it from sibling tools like food_search (generic search) and food_retail_prices (pricing-only). The verb-resource combination is 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 implies usage for retrieving Kroger product details but does not explicitly state when to use it vs. alternatives (e.g., food_search, food_retail_prices). No guidance on prerequisites or exclusions. The term 'Kroger-specific' provides some context, but explicit when-not-to-use or alternative tool references would improve clarity.

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 discloses that it reads the persistent profile, scores meals, returns ranked picks with a buy URL and signature, and optionally writes to cross-AI memory. With no annotations provided, this covers key 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?

The description is four sentences, each adding value: purpose, mechanism, output format, and optional side effect. It is front-loaded and efficient, though slightly lengthy for a brief summary.

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 sufficiently explains the tool's operation for an AI agent given 10 parameters and no output schema. It covers input usage and output structure (ranked picks, URL, signature), lacking only explicit return field details.

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 good parameter descriptions, and the text adds context (e.g., profile_hash reads from /p/{hash}, agent_id enables memory writes), enhancing 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 recommends meals matched to a user bridge profile, explains the process (reading profile, scoring, returning ranked picks with buy URL and signature), and distinguishes itself from sibling tools by focusing on meal recommendation rather than pricing or 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?

The description indicates when to use the tool (recommending meals based on a user profile) and briefly mentions optional memory writing, but does not explicitly state when not to use it or list alternative tools for different tasks.

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 should disclose behavioral traits. It mentions showing discovery lag but does not describe output format, whether it's read-only, or any side effects. The behavior is mostly implied.

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 at three sentences and front-loads the main purpose. No unnecessary words, but could be slightly more 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 three parameters, no output schema, and low schema coverage, the description is incomplete. It does not explain the discovery lag concept in detail, output structure, or 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 only 33% (only 'days' has a description). The tool description does not add meaning to the 'limit' or 'source' parameters beyond the schema, and 'source' options are not explained despite being mentioned.

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 finds recently added food products across all data sources, and distinguishes it from siblings by focusing on new products and discovery lag.

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 exploring new product data and discovery lag, but does not explicitly state when to use this tool versus alternatives like food_search or food_compare.

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?

Given no annotations, the description discloses the conditional behavior (Kroger data if product exists) and the read-only nature. It does not mention authentication needs or rate limits, but the core behavioral traits are clear.

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 core purpose, and every sentence adds value. No redundant or 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?

No output schema exists, so the description reasonably explains return values (calories, protein, etc.) and conditional Kroger fields. It lacks guidance on input selection logic (which param to use) but covers the key information for an AI agent.

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 description reuses parameter names from the schema without adding extra meaning. The schema itself already describes each param, so the description adds no new semantic value beyond what's in 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 nutrition facts for a specific food product, listing specific nutrients and Kroger-specific data. It distinguishes from sibling tools by focusing on nutrition facts plus optional retail data.

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 nutrition lookup but provides no explicit guidance on when to use this tool versus alternatives like food_kroger_product or food_search. No when-not or alternative suggestions.

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 full burden. It states the tool compares prices across levels and shows markup, implying a read-only operation. However, it does not mention data freshness, rate limits, or whether the comparison includes historical data. The behavioral profile is partially clear but lacks important details.

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. The first delivers the core purpose and scope. The second adds a clear value proposition ('Great for understanding food economics'). No filler words. Very compact and front-loaded.

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 agent must infer return format from the description. The description explains what the tool does (compare levels, show markup) but not the structure of results (e.g., per-level price, markup percentage, timestamp). For a tool with no annotations and no output schema, this is a moderate gap. It covers the 'what' but not the 'how' of returns.

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%: the single 'product' parameter has a description listing examples. The tool description reiterates 'eggs, ground beef, lettuce, milk, etc.' which adds marginal value. With full schema coverage, the baseline is 3, and the description does not provide additional semantics (e.g., case sensitivity, accepted formats).

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 specific verbs and nouns: 'Compare price levels for a food product across the supply chain'. It lists concrete levels (farm gate, wholesale, retail, Kroger) and notes it shows markup. This clearly distinguishes from sibling tools like 'food_commodity_prices' (likely broader) and 'food_retail_prices' (likely less granular).

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 ends with 'Great for understanding food economics', which implies a use case but does not explicitly state when to use this tool versus alternatives. With 17 sibling tools, more guidance (e.g., 'Use for supply chain markup analysis; for raw commodity prices, see food_commodity_prices') would help the agent. Current guidance is vague.

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 specifies the tool is a read operation ('Read') and notes the profile URL is persistent and accessible by any AI, indicating read-only behavior. With no annotations provided, this adequately covers the main 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?

Three concise sentences: first lists contents, second gives URL format, third states purpose. No unnecessary words, and key information is front-loaded.

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 simplicity (one parameter, no output schema), the description sufficiently covers what the tool does, its inputs, and intended use. The lack of output schema is partially compensated by listing the returned fields in the description.

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 describes the profile_hash parameter (100% coverage). The description adds valuable context by explaining the URL format (food.rootz.global/p/{hash}) and referring to it as Layer 9, which 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 reads a user food profile, enumerates specific contents (household members, dietary constraints, etc.), and distinguishes it from sibling tools like food_commodity_prices or food_diet_filter.

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 explicitly advises using this tool to personalize food recommendations, providing clear context. However, it does not mention when to avoid using it or alternative tools.

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, so description carries full burden. Only states the basic action, no disclosure of what happens when no recall exists, authentication needs, or 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 with minimal waste. First sentence is effective; second sentence adds little value. Could be more concise but is not overly verbose.

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, yet description fails to explain the return format (e.g., boolean, list, or text). Agent cannot predict what to expect from the 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 both parameters documented. Description adds no extra meaning beyond the schema; 'Critical for food safety awareness' is redundant. 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?

Description clearly states the action ('Check'), the resource ('specific food product or brand'), and the scope ('active or recent recalls'). Effectively distinguishes from sibling 'food_recall_search' by focusing on a single product check.

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 guidance provided on when to use this tool versus alternatives like 'food_recall_search'. Agent must infer context from sibling names alone.

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 present, so the description carries full responsibility. It discloses the data source (openFDA, public domain) and temporal coverage, which is useful. However, it omits behavioral traits such as rate limits, authentication needs, or whether the operation is read-only (presumably yes). It also lacks details on pagination or error handling.

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: two sentences that front-load the purpose and efficiently cover scope and source. Every word earns its place 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 5-parameter tool with no output schema and no annotations, the description gives a reasonable overview of purpose and data source. However, it lacks information about return format, pagination, and the mismatch between 'date range' and the actual 'year' parameter. The sibling 'food_recall_check' suggests a more targeted tool, but no comparisons are made.

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 4 of 5 parameters with descriptions (80% coverage), so the schema does the heavy lifting. The description adds high-level context ('product, company, reason, or date range') but uses 'date range' loosely when only 'year' is a parameter. This slight mismatch does not add significant value 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 uses the specific verb 'search' and resource 'FDA food recalls', explicitly mentioning searchable dimensions (product, company, reason, date range) and coverage (2004-present). This clearly distinguishes it from siblings like 'food_recall_check'.

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 no explicit guidance on when to use this tool versus alternatives. With 17 sibling tools, including 'food_recall_check', the absence of selection criteria or exclusion cases leaves the agent without clear decision support.

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 behavioral traits. It lists outputs (nutrition, ingredients, allergens, recalls, total meal) but does not mention performance, authorization requirements, or error handling.

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 essential information in the first. The second sentence is marketing fluff but not harmful. Could be slightly tighter.

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 output schema, the description thoroughly explains the return values (nutrition, ingredient analysis, allergen warnings, recall checks, total meal nutrition), making it complete for a lookup tool with one 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 coverage is 100%, so baseline is 3. The description adds context that the UPCs come from a receipt but does not specify format constraints or behavior for invalid codes.

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 takes UPC barcodes from a receipt/shopping list and returns comprehensive nutrition, ingredient, allergen, recall, and total meal analysis. It distinguishes itself as a 'receipt-to-nutrition bridge' that no other sibling tool 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 implies usage when you have a list of UPC barcodes from a receipt, but does not explicitly state when to avoid it or compare with sibling tools like food_nutrition or food_recall_check, which might handle subsets.

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 provided, the description carries the full burden. It implies a read operation and describes data source and frequency, but does not explicitly state read-only behavior, rate limits, or what happens on missing items.

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 with three short sentences, front-loaded with purpose, 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?

For a simple 2-parameter tool with no output schema and no annotations, the description provides adequate context: data source, item scope, frequency, and trend display. Could mention return format or data structure.

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% so baseline is 3. The description adds example items and context about scope (~70 items) but no extra parameter details 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 average retail prices for common grocery items from BLS CPI data, but does not explicitly differentiate from sibling tools like food_commodity_prices or food_price_compare.

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 guidance on when to use this tool vs alternatives, nor any prerequisites or exclusions. The description only states what it does.

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 present, so the description must cover behavioral traits. It mentions real-time Kroger pricing and data source aggregation but does not disclose any limitations, rate limits, or side effects. It is a search tool and likely read-only, but that is not explicitly stated.

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 three sentences, front-loaded with the core purpose. It efficiently lists what is returned and the data sources without redundancy. 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 no output schema, the description partially covers return values by listing nutrition, ingredients, etc. It omits details like pagination, error handling, or response format. For a search tool, it is fairly complete but could improve with more behavioral 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 baseline is 3. The description adds context about search types (name, brand, UPC) matching the query parameter, but does not provide additional semantics beyond the schema for other parameters. It does mention real-time pricing indirectly but not as parameter detail.

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 for food products by name, brand, or UPC barcode, and lists the returned data (nutrition, ingredients, allergens, pricing, aisle location, data source origin). It distinguishes from siblings like food_compare or food_commodity_prices by specifying its broad search and aggregation capability.

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 general product search but does not explicitly state when to use this tool vs alternatives, nor does it provide exclusions or prerequisites. Given the sibling list, it is the primary search tool, but guidance is absent.

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 states it shows journey data but lacks details on data freshness, read-only nature, or any limitations beyond demo population.

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 clear front-loaded purpose. No redundancy, each 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 no output schema and no annotations, description should elaborate on return format or data structure. Current version is adequate but incomplete for practical 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 coverage is 100% with both parameters described. Description adds context that product_id and product_name are searchable but no additional semantic value 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?

Describes a specific verb 'View' and resource 'supply chain/traceability data' for a product. Lists journey stages (farm-to-shelf) and distinguishes from siblings like food_recall_check or food_nutrition.

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?

Mentions demo product population and design for real-time integration, implying current limitations but no explicit when-to-use or alternatives compared to siblings.

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