Food Facts

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

Annotations already cover the safe-read profile (readOnly, idempotent, non-destructive, openWorld), and the description goes well beyond them: it calls out that tags/ingredients are crowd-sourced and may be wrong, that this is a data lookup rather than a safety guarantee, and that the physical label wins. That is exactly the kind of trust/limitation context an agent needs before relaying allergen results to a user.

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?

Front-loaded with the usage trigger and example, followed by return-value summary and then the caveat. Every sentence adds distinct information (trigger, inputs, outputs, trust limits) with no filler 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?

Despite an output schema existing, the description usefully summarizes the four result categories (contained, possible traces, not listed, no data), and the annotations cover safety semantics. An agent has everything needed to select and correctly frame the call.

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% – both barcode and avoid are documented with format and examples – so the schema carries the parameter burden. The description restates the two inputs but adds no format, ordering, or matching semantics (e.g., substring vs exact allergen matching) beyond the schema, making the baseline 3 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?

States a specific verb and resource (check allergens for a product) and scopes it to a barcode plus an avoid-list. The example query makes the intent unmistakable, and it is clearly distinguishable from siblings like lookup_barcode or search_products, which do not perform allergen matching.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit trigger condition ('when the user asks whether a product contains certain allergens') plus a sample utterance, which is strong usage guidance. It does not, however, contrast itself with lookup_barcode or any other sibling, nor state when not to use it, so it falls short of the top band.

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 cover the safety profile (readOnly, idempotent, non-destructive), so the bar is lower. The description adds genuinely useful behavioral context beyond them: the ranking is lowest-to-highest per 100 g, invalid/not-found/no-data barcodes are reported back, and the 'Crowd-sourced data: check the physical labels' caveat warns about data reliability.

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?

Four tightly packed sentences with no filler; usage and the key constraint lead, and the data-quality warning closes. It is dense but each sentence carries distinct information, so it stays on the right side of conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description need not detail return values, yet it summarizes the ranking, included nutrition fields, and error list. Combined with annotation-covered safety and full schema coverage, only the lack of explicit sibling routing keeps it from being fully 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 100%, so both parameters (field enum and barcodes array) are already fully documented in the schema. The description restates the sugars default and the 1-5 range without adding format or edge-case detail beyond the schema, so the baseline 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?

States a specific verb and resource ('compare packaged foods') and adds a concrete example ('which of these barcodes has the least sugar?') that pins the domain to packaged foods. It does not explicitly distinguish itself from siblings like lookup_barcode or search_products, so an agent must infer the multi-barcode comparison role rather than being told.

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 opening 'Use this when...' gives a clear trigger condition with an illustrative query, and the parameter sentence clarifies the 1-5 barcode constraint. It stops short of explicit when-not guidance or naming the alternative tools (e.g., single lookup vs. comparison), leaving that routing to inference.

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 readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context beyond that: the tool is pollable (pending until a reply arrives, then answered) and carries an explicit trust boundary warning that reply content is data, not instructions — a meaningful prompt-injection disclosure.

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 tight sentences: purpose first, then polling behavior, then the trust caveat. Every sentence carries distinct information and nothing is padded.

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 single-parameter read tool with an output schema and full annotation coverage, nothing an agent needs is missing. The polling semantics and the injection warning go beyond what structured fields provide, closing the remaining gaps an agent would otherwise have to discover by trial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter exists and the schema already documents it fully (coverage 100%, including the ^fb_[0-9a-f]{32}$ pattern and the note that submit_feedback returned it), so the baseline of 3 applies. The description repeats the ticket-id linkage but adds no format or validation detail 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?

States a specific verb (Read) and resource (maintainers' reply to feedback), and ties it causally to the sibling tool submit_feedback via the ticket id. An agent can distinguish it from submit_feedback, screen_token, and wallet_identity without opening any schema.

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?

Clearly establishes the precondition: this is used after submit_feedback, with the ticket id that call returned. It does not state any exclusions or alternative ways to retrieve feedback status, but the usage context is unambiguous.

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?

Beyond the read-only/idempotent/open-world annotations, the description discloses real behavioral traits: the check digit is validated before lookup, the underlying data is crowd-sourced and may be incomplete or wrong, and the output is explicitly not dietary or medical advice. These provenance, validation and liability caveats materially change how an agent should present results.

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?

Front-loaded with the trigger condition and example, then caveats, which is the right order. The long enumeration of return fields is somewhat listy and partly duplicates the output schema, so it is efficient but not maximally tight.

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 single-parameter lookup with an output schema and full annotations, the description covers trigger, validation, data-quality caveats and disclaimer, so nothing an agent needs to call and interpret the tool correctly is missing.

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% and the barcode property already documents 'EAN or UPC barcode, 8 to 14 digits' with min/max length. The description's 'EAN or UPC' phrasing repeats the schema rather than adding format, normalization or leading-zero handling detail, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (look up a product barcode) and immediately scopes it to EAN/UPC input with a concrete example query. It also enumerates the returned fields, so an agent can tell it apart from search_products, compare_products and check_allergens without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit trigger: 'Use this when the user gives a product barcode (EAN or UPC) and wants to know what is in it,' reinforced with a sample utterance. It does not, however, name the sibling alternatives (e.g. text search via search_products) or state when not to use it, so routing against siblings is left partly to inference.

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 readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds useful behavioral context beyond that: the result is a summary (barcode, name, brands, Nutri-Score) and the full entry requires lookup_barcode, which tells the agent this tool truncates data rather than returning everything.

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 tight sentences: trigger condition and examples first, parameter guidance second, return shape and alternative last. Every sentence carries information an agent needs to select and call the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description need not enumerate return values, and it still summarizes them for routing purposes. For a 3-parameter read-only search it covers trigger, parameters, result scope and the alternative tool, leaving no material gap.

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 query, country format and the limit cap of 10 are already documented in the schema. The description restates the same facts (country as English name or two-letter code, limit up to 10) without adding syntax or format detail beyond it, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('find food products by name or brand') and scopes it with concrete examples ('find oat milks', 'Nutella jars in Germany'). It also distinguishes itself from the sibling lookup_barcode by assigning full-entry retrieval to that 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?

Opens with an explicit trigger condition ('Use this when the user wants to find food products by name or brand') and names the alternative ('use lookup_barcode for the full entry') along with the condition that selects it. Nothing about when to choose this tool is left to inference.

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?

Adds substantial context beyond the annotations: it returns a ticket id, feedback is explicitly not a chat and nothing in it is executed, and links/emails/phone numbers are stripped with nothing stored about the caller. That privacy and no-execution disclosure is genuinely useful given openWorldHint=true and readOnlyHint=false.

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?

Front-loads what the tool sends, then what to pass, then what it returns, in three dense sentences with no filler. Every clause carries actionable 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?

Covers purpose, the enumerated kinds, the optional tool/error inputs, the 1000-character bound, the return value (ticket id), and privacy handling. With an output schema present, nothing further is required for an agent to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all four parameters including the kind enum. The description restates the message length limit and the optionality of tool/error ('if you know them') but adds no syntax or format detail beyond the schema, making the baseline 3 correct.

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?

States a specific verb and resource ('Send feedback, a bug report or a tool request to the maintainers of the tools on this server') and enumerates the concrete situations (missing tool, stale data, broken/wrong answer). It is clearly distinguishable from the read-side sibling get_feedback_reply.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear triggering conditions for each kind (need_tool, need_data, bug, other) and tells the agent to include the tool name and error text when known. It does not explicitly name the alternative get_feedback_reply or state when NOT to use this tool, so it falls short of full routing guidance.

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