EU Safety Gate Product Recalls

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

With no annotations, the description carries the full burden and meets it well. It discloses the data source (46,506 official alerts since 2005), the search keys, and the three possible verdicts ('recalled', 'possible_match', 'clear'). It also provides the critical caveat that a 'clear' verdict is not a safety certification. This adds meaningful behavioral context beyond any structured fields.

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 with no redundancy. The first sentence states the core purpose, the second provides operational detail (database size, search fields, output), and the third clarifies an important limitation. Every sentence earns its place.

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 search tool, the description is complete: it specifies input criteria, output verdicts, and a caveat about interpretation. It does not explain how parameters combine or how to handle ambiguous matches, but given the absence of an output schema and the tool's straightforward nature, this is largely 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 100%, with each parameter already described in the schema (e.g., gtin's reliability, brand's German variant handling). The description adds only a restatement that searches are by 'GTIN/EAN barcode, brand and/or product name', which adds minimal value over the schema. 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?

The description clearly states the tool's purpose: 'Check whether a product has been recalled in the EU.' It specifies the resource (EU Safety Gate/RAPEX alerts), the verb (check), and the scope (searches 46,506 alerts since 2005 by GTIN, brand, product). This distinguishes it from sibling tools like get_brand_recall_history (brand history) and list_recent_recalls (recent alerts).

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 intended usage is clear from the first sentence: to determine if a product is recalled. The description also mentions the search criteria (GTIN/EAN, brand, product name), implying when to use it. However, it does not explicitly reference sibling tools or state when not to use them, so it falls short of full 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?

With no annotations provided, the description carries the burden of disclosing behavior. It does this well by summarizing the output content (count, period, serious risk count, hazard types, product categories, countries of origin), implying a read-only operation and giving the agent a clear sense of what to expect. It does not mention any rate limits or data freshness, but for a simple retrieval tool this is adequate.

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 sentences, with the action and resource front-loaded. Every word adds value, from the specific output details to the use case, with no redundant or filler content.

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 tool with no output schema, the description is remarkably complete. It explains what data will be returned (counts, categories, origins) and suggests when to use it, making it fully self-contained for an agent deciding to invoke it.

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 parameter 'brand' is already described as 'Brand or manufacturer name.' The tool description reiterates the parameter's role ('history of a brand') without adding new details such as format, length limits, or examples. Thus it meets the baseline for high schema coverage but does not exceed it.

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

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

The description clearly states the tool's function: 'Get the EU recall history of a brand'. It specifies the resource (brand) and the action (get history), and distinguishes itself from siblings like check_product_recall (likely product-level) and list_recent_recalls (likely recent alerts) by focusing on brand-level EU recall history with aggregated statistics.

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 a clear use case: 'Useful for supplier and counterparty risk screening', indicating when this tool is appropriate. However, it does not explicitly mention when not to use it or recommend alternatives, so it lacks explicit 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, the description carries the full burden. It adds useful behavioral context by noting alerts are 'updated daily' and include 'official corrections and withdrawals,' indicating data freshness and content scope. It does not mention side effects, but since this is a read-only list tool, this is acceptable.

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 sentences) and front-loaded with the core purpose. The second sentence adds valuable update-frequency and content nuance without 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?

For a simple read-only list tool with 4 optional parameters and no output schema, the description adequately covers purpose, filters, and update behavior. It does not describe the return format, but given the tool's simplicity and that output schema is absent, this is a minor 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 baseline is 3. The description's mention of filters (country, category, risk level) simply echoes the schema parameter descriptions without adding additional syntax, constraints, or examples beyond what the schema already 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 clearly states the tool lists 'the most recent EU Safety Gate product recall alerts', using a specific verb and resource. It also mentions optional filters, distinguishing it from sibling tools like check_product_recall and get_brand_recall_history by focusing on recent alerts rather than individual lookups or brand history.

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

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

The description implies when to use the tool (to get recent alerts) and lists available filters, providing clear context. However, it does not explicitly contrast with alternative tools or state when not to use it, so it falls short of the explicit guidance required for a 5.

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