Cycle Intelligence

create_data_carrier

Use this tool to generate the printable data carrier (QR content) of a twin: a GS1 Digital Link URL (Sunrise 2027 standard) if you pass a valid GTIN, or a serialized twin URL fallback without one. The URL resolves to the product's live twin the moment it is created - it is connected, not a loose QR. Invalid GTINs are rejected before printing. Requires an organisation API key. OPTIONAL: pass allocation={channel|retailer, shipment?, region?, units?} to link the serial to its intended retail channel (channel-aware serialization: future scans of that unit resolve their channel, always labelled INFERRED).

Input Schema

TableJSON Schema

Name	Required	Description
`sku`	No
`gtin`	No
`serial`	No
`allocation`	No	optional intended retail channel: {channel, retailer, shipment, region, units}
`product_id`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedSep 4, 2026

addedInput schema / properties / allocation

Added value: +{
+  "description": "optional intended retail channel: {channel, retailer, shipment, region, units}",
+  "type": "object"
+}

First observedSep 1, 2026

TDQS

A3.6/5.0

Behavior3/5

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.

Conciseness3/5

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.

Completeness2/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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TDQS

A4.2/5.0

Disambiguation5/5

Each tool has a distinct purpose: regulatory info, readiness validation, twin creation, DPP issuance, data carrier generation, and identifier validation. No two tools overlap in function; create_product_twin and create_dpp are complementary steps in the same workflow.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: check_, create_, validate_. The verbs are consistent and the nouns clearly indicate the target object or action, making the API predictable and easy to navigate.

Tool Count5/5

With 6 tools, the server is well-scoped for the DPP lifecycle: requirements, readiness, twin creation, DPP issuance, carrier generation, and identifier validation. Each tool is essential and the count is appropriate for the domain.

Completeness4/5

The core workflow of creating a compliant DPP is covered (validate readiness, create twin, create DPP, generate carrier). Missing update/delete operations are likely out of scope for this server, but the absence of a retrieval or listing tool is a minor gap that agents could work around via the returned URLs.

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