triggerfetch

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

Annotations already establish readOnly, idempotent, non-destructive and open-world behavior, and the description adds genuinely new context on top of them: a rate limit (one free check per day), an authentication path (the brand's agent signs in afterward), and the fact that results reflect what "the AIs in the index" currently name. These are exactly the operational facts annotations cannot express, though error and failure modes for an unknown barcode or country are not covered.

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?

It is front-loaded with a hook rather than the operation, and the core content is packed into two long parenthetical clauses that are hard to parse on a first read. Nothing is strictly wasteful, but the marketing register and nested lists make it slower to extract the actionable facts than a plain verb-plus-scope sentence would.

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?

Because an output schema exists, the description need not spend most of its length explaining return fields, yet it does, while leaving input formats, the meaning of "the index," and the session/auth requirement under-specified. For a three-parameter, open-world tool with no schema descriptions, the definition is serviceable but leaves real gaps an agent would hit on first 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 0%, so the description carries the full burden of parameter meaning, and it only partially delivers: it establishes that barcode and country are singular, scoped inputs (one barcode, one country) but gives no format guidance (EAN/UPC, ISO code vs country name). The session parameter is only obliquely referenced via "the brand's agent signs in," so its role as an authentication token must be guessed.

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 names a concrete resource and scope: it checks whether one product, identified by a barcode in one country, appears in AI answers, and it enumerates the returned facets (found, likely why, corrected). However, the rhetorical lead ("Is your product in the answer when a shopper asks?") delays the actual statement of purpose, and the mention of a "corrected" output blurs the boundary with the sibling correct_product tool, so it does not fully separate the read-only check from the write path.

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?

Usage context is implied rather than stated: the tool is for a brand checking one barcode in one country, and the free-per-day boundary signals when a signed-in agent must take over. The description never explicitly says when to use this instead of correct_product or get_index, nor what prerequisites (sign-in, verified brand record) apply before the first check, so the agent must infer the routing.

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 non-read-only, non-idempotent, open-world, and non-destructive, and the description adds real context beyond them: it names two external side effects (a new match record on matchfetch, a new receipt on rco.wefetch.ai) and states the new receipt supersedes the prior one. It also discloses that the outcome is only observable in the next index edition rather than immediately, which is exactly the kind of delayed-effect detail annotations cannot carry. It stops short of saying whether a correction can be undone or re-submitted safely.

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 dense sentences with no filler, and the write effects are front-loaded after the opening clause. The leading 'Corrected:' fragment is stylistically odd and slightly obscures the subject, but nothing in the text is redundant.

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 mutation tool with an output schema already covering return values, the description does the important work: external writes, supersession of the prior receipt, and delayed visibility. What is missing is modest — the role of the 'session' parameter and any failure/rejection behaviour for an open-world write.

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 0%, so the description must carry parameter meaning, and it partially does: 'words' is unpacked as name/brand/size, and barcode and country are scoped to a single pair. The fourth required parameter, 'session', is never mentioned, and no format guidance is given (country code style, how to compose the words string, barcode format).

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 conveys that a signed-in brand submits the search words (name, brand, size) for a single barcode/country pair, which is the core action. However, the phrasing is oblique ('gives the words it wants its product found by') and it never distinguishes this tool from the sibling check_product, which an agent would plausibly reach for first when investigating a wrong match.

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?

There is an implicit prerequisite in 'the signed-in brand', signalling authentication is required, and the closing line hints the payoff arrives in the next index edition. But there is no explicit statement of when to use this versus check_product, no exclusions, and no note about what to do if the correction needs review or reversal.

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, openWorldHint, and destructiveHint=false, so safety is covered. The description adds that this is a 'current edition' snapshot, implying a point-in-time dataset that changes over editions, but it says nothing about result size, pagination, or what the open-world data source is. An output schema exists, so return shape need not be explained.

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?

A single dense sentence that front-loads the resource identity and then enumerates contents; nothing is wasted. It is long and clause-heavy, but every clause describes actual returned data rather than filler.

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 content description is largely sufficient for understanding return values, and annotations cover the safety profile. The gap is operational: with 0% schema coverage and no enum hints, an agent still lacks the value format and empty-default behavior needed to call it confidently.

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 0% for both optional parameters, so the description carries the full burden. It only implies that 'category' and 'country' are filter dimensions of the index; it never says what value format to pass (ISO code vs. name), nor what an empty default means — presumably all categories and countries, but that is left to inference.

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 names a specific resource, 'The AI Grocery Shopping Index', and enumerates its contents: AI answers by category and country, per-product barcode, answer share, shelf position, and orderable coverage. An agent can tell this is a reference dataset fetch, distinct from the product-level siblings check_product and correct_product, though the description never uses a verb like 'returns' or 'retrieves'.

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?

There is no when-to-use guidance, no mention of the sibling tools, and no statement of when this index is preferable to check_product for a single item. The agent must infer usage entirely from the content list.

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 only say this is a non-read-only, non-idempotent, open-world call; the description adds the crucial behavior that this is a challenge-response step whose returned token must be published at a well-known URL or DNS TXT record before verification. It omits token lifetime and whether a second call invalidates an earlier token, which matters given idempotentHint=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?

Two sentences with zero filler: the identity/inputs sentence comes first, followed by the placement-and-verify instruction an agent needs to act. Every clause is load-bearing.

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 return token needn't be described, and the description still usefully explains what to do with it. For a non-idempotent, open-world auth flow, the remaining gap is failure handling and token expiry, but nothing needed to make the first correct call 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 coverage is 0% and both parameters are bare strings, so the description carries the full burden. It supplies real meaning for both: card_url is 'the card address the brand claimed its seat with' and barcode is 'one of its barcodes', though it gives no format hints (full URL vs bare domain, barcode encoding) to prevent malformed input.

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 ('Sign in the brand's agent with its claim_seat identity') and names exactly what inputs identify the brand. The reference to 'then call sign_in_verify' makes the two-step flow unambiguous versus its sibling of the same name.

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 establishes the precondition (the brand has a claim_seat identity and a barcode) and names the required follow-up tool, sign_in_verify, which routes the agent correctly. It doesn't state exclusions or what to do if the placement challenge fails, so it falls short of a full when/when-not treatment.

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 declare it is not read-only, not idempotent, and open-world; the description corroborates the open-world aspect by describing a token read at the brand's domain. It does not disclose side effects, whether re-calling invalidates the challenge, or what happens on seat mismatch, leaving real gaps for a non-idempotent auth-completing operation.

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 purpose statement is front-loaded, which is good, but the single dense sentence packs unexplained domain jargon ("seat on the hub", "that card") that costs more than it conveys. Tight, but not 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?

An output schema exists, so return-format explanation is unnecessary, and the description does note the session key is returned. However, for an auth-completing, non-idempotent tool the description omits the prerequisite chain (obtaining challenge_id) and failure behavior, which an agent needs to invoke 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?

The single parameter challenge_id has 0% schema description coverage, so the description must carry the meaning. It never mentions challenge_id at all, referring instead to tokens, barcodes and seats, leaving the agent to guess where the identifier comes from and what form it takes.

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?

"Finish sign-in" gives a verb and resource, and the closing clause ties the result to check_product/correct_product, but the middle clause ("the token is read at the brand's domain and the barcode's seat on the hub must be the seat of that card") is jargon that does not clarify what the tool actually does. An agent knows it completes a sign-in flow but not concretely what's validated.

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 "Finish sign-in" and the mention that it yields the session key for check_product and correct_product implies it is the step after sign_in and before those tools. There is no explicit when-not guidance or statement of prerequisites (e.g., that a challenge_id must come from sign_in).

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