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Cycle Intelligence

by com.cycle-platform

validate_dpp_readiness

Read-only

Use this tool when a company needs to know whether its product catalogue is READY for the EU Digital Product Passport (DPP/ESPR): pass an array of products (any reasonable JSON shape: name, sku, description, brand, composition/material, country of origin, supplier, care instructions...) and get a rule-based readiness score 0-100 per product plus the exact missing fields, and an aggregate with the single field that unlocks the most products. INPUT: array of product objects (flexible keys). OUTPUT: per-product score, ready flag, missing field labels; aggregate with top_missing_fields and unlock_hint. Deterministic rule-based validation (ESPR mandatory field set). Up to 200 products per call. No account needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
productsYesProducts to validate. Flexible keys accepted: name/title, sku, description, brand/vendor, composition/material/fabric, country/origin/manufacturing_country, supplier, care/care_instructions, compliance, certifications.

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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.

Conciseness4/5

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.

Completeness5/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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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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