Cycle Intelligence

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

Annotations already indicate readOnlyHint and openWorldHint, and the description goes further by stating the tool is deterministic, requires no account, is informational not legal advice, and clearly distinguishes in-force rules from pending delegated acts. This adds useful behavioral context about reliability and scope.

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 compact, well-organized, and front-loaded with the primary use case. Each sentence serves a purpose: when to use, what it returns, and behavioral caveats. No filler or redundant repetition of the tool name or annotations.

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 tool with one optional parameter, no required fields, and read-only annotations, the description is complete. It explains the output substance (timeline, mandatory data fields, in-force vs pending notes), which is especially valuable given there is no output schema. It also covers the information-versus-legal-advice boundary.

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 provides 100% coverage for the single optional `sector` parameter, including its default value. The description reinforces the relevance of textile/fashion products but does not add substantial new meaning beyond what the schema supplies. 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?

The description uses specific verbs and resources: it answers WHAT the DPP requires, WHEN it becomes mandatory, and which milestones apply. It clearly positions itself as an informational tool returning verified regulatory timelines and mandatory data fields, which distinguishes it from sibling tools that create data carriers, DPPS, product twins, or validate readiness.

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 states when to use the tool ('Use this tool when a company or user asks WHAT... WHEN...'). It gives clear context and examples, though it does not explicitly name alternative sibling tools or state when not to use it. The triggers are specific enough that an agent can route correctly.

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?

It mentions rejection of invalid GTINs, requirement for an API key, and that the URL resolves to a live twin. However, it does not describe the response format, potential side effects like updating allocation, or other error cases. It is not contradictory to annotations.

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 verbose with multiple clauses and some redundant phrasing like 'it is connected, not a loose QR'. While it packs useful information, it could be more concise without losing meaning.

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?

It does not describe the output of the tool (what exactly is returned), nor does it explain the necessity or usage of sku and product_id. It also lacks details on error handling beyond invalid GTIN, making it incomplete for a robust agent to 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?

The description explains the role of gtin (validity, rejection), allocation (optional, channel-aware, with format), and serial (via fallback URL), but does not touch on sku or product_id. Since only allocation has a schema description, the description adds some value but leaves two parameters unexplained.

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 its purpose: generating a printable data carrier (QR content) for a twin, with two modes based on GTIN validity. It distinguishes from siblings like create_dpp or validate_gs1_identifier by focusing on the carrier generation.

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

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

It explicitly says 'Use this tool to generate...' providing a direct instruction. It explains when to use it (when you need a QR for a twin) and mentions behavior for invalid GTINs, but does not explicitly reference alternative tools or when not to use it.

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 goes far beyond the sparse annotations: it reveals that Cycle validates ESPR conformance first, refuses incomplete passports, returns the exact missing fields, outputs the DPP dataset plus public URL, queues EU registry submission, and requires an organisation API key. It also does not contradict the annotations.

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 dense but every sentence contributes operational value: purpose, validation behavior, output, registry side effect, and auth requirement. It is front-loaded and avoids redundant restatement of the tool name.

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?

It covers auth, validation, failure behavior, return output, and the registry side effect, which provides a strong overall picture. The missing piece is parameter semantics, which is a significant gap for a 2-parameter tool with no schema descriptions.

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%, and the description never explains what sku or product_id mean, which is required, or how they identify the twin. An agent cannot reliably know whether to pass one, both, or what values they should come from.

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 a specific action - 'issue the Digital Product Passport of a twin' - and ties it to the precedent tool 'create_product_twin', which differentiates it from validation or requirement-checking siblings. The title 'Create a conformant DPP' reinforces the purpose.

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?

It explicitly positions this tool as the follow-up to create_product_twin and describes the conformance gate, giving clear context for when to use it. It does not explicitly name exclusionary conditions or alternatives like check_dpp_requirements or validate_dpp_readiness, but the predecessor relationship is a strong usage signal.

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 signal non-read-only/non-destructive, so the description adds real value by noting the required Authorization Bearer API key, the 100-product batch cap, and the persistent nature of the returned twin ID/URL. It does not detail failure modes, but for a create operation the key side effects are 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?

Four compact sentences cover purpose, preconditions, input, output, auth, and batch limit with no filler. The most important usage cue 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?

With a single array parameter and no output schema, the description provides everything an agent needs: prerequisite, input shape reference, per-product outputs, auth requirement, and batch ceiling. It is complete for the tool's complexity.

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%, so the description must compensate. It does: 'array of products' is explained as the same flexible shape as validate_dpp_readiness, and the 100-per-call limit is stated. However, it relies on cross-referencing another tool for the actual product object structure rather than being self-contained.

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 names the operation ('CREATE live Digital Product Twins in Cycle from its product data') and references the validation sibling, so an agent can tell it apart from the read/validation tools. However, it does not explicitly differentiate from the create_dpp or create_data_carrier siblings, which could still be mistaken as alternatives.

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?

It opens with an explicit 'Use this tool when...' and adds the prerequisite of validating with validate_dpp_readiness, giving clear context for when the tool applies. It stops short of saying when not to use it or naming alternative creation 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?

Annotations already mark the tool read-only, and the description adds meaningful behavioral detail beyond that: deterministic rule-based validation, no account needed, a 200-product limit, and the exact form of the output including missing fields and aggregate unlock hints. This gives an agent a clear model of what the tool will and will not do.

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 front-loaded with the use case, then input, output, and constraints in a logical order. There is slight redundancy between 'exact missing fields' and 'missing field labels', and 'flexible keys' is stated twice, but overall every sentence carries useful 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?

With no output schema, the description carries the full burden of explaining return values, and it does so clearly: per-product score 0-100, ready flag, missing field labels, aggregate top_missing_fields, and unlock_hint. Combined with the deterministic rule mention, input examples, and usage constraints, an agent has enough to invoke and interpret the result 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 schema already documents the `products` parameter with 100% coverage and flexible keys. The description adds value by clarifying that any reasonable JSON shape is accepted, giving representative fields, and adding the 200-product-per-call limit that is not present 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 states a precise action: determining whether a product catalogue is ready for the EU Digital Product Passport (DPP/ESPR) and producing a readiness score. It distinguishes this from siblings such as checking requirements or creating DPPs/data carriers by focusing on readiness scoring of existing products.

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

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

It opens with an explicit use case: 'Use this tool when a company needs to know whether its product catalogue is READY...' and describes the expected input. It does not name alternatives or give when-not-to-use guidance, but the context is clear enough for an agent to select it.

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 readOnlyHint annotation, the description discloses that the tool is deterministic, requires no account, and returns the validity status, normalized GTIN-14, and exact error on failure. This gives the agent a strong model of what to expect without needing to invoke the tool.

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 efficient: three sentences, with the main purpose front-loaded and supporting behavior placed at the end. Some parenthetical detail about Sunrise 2027 and DPP is informative but not strictly necessary, keeping it from a perfect score.

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 tool's inputs, outputs, and usage timing, which is strong given there is no output schema. It does not explicitly state what happens when both gtin and digital_link are provided or when neither is provided, which is a minor gap for a tool with zero required parameters.

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 the description adds no essential parameter meaning beyond what the schema already documents. It does clarify that only one identifier type (GTIN or Digital Link) is expected at a time, but the individual parameter descriptions already carry the semantic load.

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 verb ('verify') and a clear resource scope (GTIN/EAN barcode numbers and GS1 Digital Link URLs). It also states the return value and error behavior, which makes the tool's purpose unambiguous and distinguishes it from sibling creation/DPP-readiness tools.

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 gives explicit guidance: use it before printing labels or QR codes to ensure GS1 conformance. It does not name alternative tools or state exclusions, but the context is clear enough for an agent to know when this validation step is appropriate.

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