Jithox EU Energy Label Preflight

Search public energy-label records

search_eprel_products

Read-only

Search the queried source by model identifier, registration number, or supplier/brand + product group. Returns CANDIDATE public records with deterministic match kinds and visible ambiguity: 'exact_public_record' / 'possible_match' / 'multiple_matches' / 'no_match_in_queried_source' — a no-match is never 'not registered'. Outage → 'unavailable'; unverified schema → 'source_schema_unverified'; stale → 'stale'. Answers come from the queried source at the stated retrieval time: the official EPREL Public API in live mode, or a clearly-labelled synthetic fixture in dev/test — provenance.sourceAuthority names which. Supplier-declared data only; Jithox verifies nothing. Preflight evidence only — never a conformity, CE, GPSR, safety or market-access determination.

Input Schema

TableJSON Schema

Name	Required	Description
`productGroup`	No	Product group to narrow the search (must be covered by the queried source). The official live source searches ONE group at a time: text searches (model/brand) REQUIRE it; registration-number lookups do not.
`modelIdentifier`	No	The supplier's model identifier to search for.
`supplierOrBrand`	No	Supplier or trade brand to narrow the search.
`registrationNumber`	No	A public registration number, when known.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes
`billing`	Yes
`product`	Yes
`receipt`	Yes
`decision`	Yes
`retryable`	Yes
`capability`	Yes
`provenance`	Yes
`generatedAt`	Yes
`limitations`	Yes
`schemaVersion`	Yes
`receiptEnvelope`	No
`decisionCategory`	Yes

TDQS

A4.4/5.0

Behavior5/5

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

The description goes well beyond the readOnly/openWorld annotations, disclosing match kinds (exact_public_record, possible_match, etc.), error states (unavailable, stale, unverified schema), source provenance (live API vs synthetic fixture), and emphasizing that it is preflight evidence only with no verification by Jithox.

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 dense and front-loaded with the core purpose, then efficiently explains match kinds and caveats. It is slightly longer than typical but every sentence contributes meaningful information without fluff.

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?

The tool has an output schema, and the description richly covers match semantics, error conditions, data source provenance, and usage constraints. Given the tool's complexity, this is complete and leaves no critical gaps.

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?

Schema descriptions cover all four parameters at 100% coverage, including the requirement for productGroup in text searches. The tool description only summarizes the search modes without adding new parameter-level detail, so it stays at the baseline of 3.

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 the tool searches by model identifier, registration number, or supplier/brand, returning candidate records with deterministic match kinds. It is specific about the resource (EPREL public records) and distinguishes it from sibling tools that compare, get, or prepare data.

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?

The description implies when to use the tool (searching by identifiers) and explains the query patterns, but it does not explicitly reference alternative sibling tools for different needs. The guidance is clear context without explicit exclusions, earning a 4.

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

All Jithox EU Energy Label Preflight tools Inspect

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TDQS

A4.4/5.0

Disambiguation4/5

The tools are mostly distinct: search finds candidates, two getters retrieve different aspects (full record vs energy label data), compare analyzes metrics, and prepare bundles receipts. The two getters could be confused, but their descriptions clearly differentiate the full record from the energy-label-specific structure.

Naming Consistency4/5

All tool names follow a verb_noun pattern with a domain prefix (eprel or energy_label), e.g., compare_eprel_product_metrics, get_eprel_product_record. The consistency is high, though 'prepare_energy_label_preflight_receipt' uses 'energy_label' instead of 'eprel', a minor deviation.

Tool Count5/5

Five tools is a well-scoped set for a preflight workflow: search, retrieve record, retrieve label data, compare metrics, and create a signed receipt. Each tool fills a distinct role with no redundancy or bloat.

Completeness4/5

The toolset covers the core preflight lifecycle: discovery, retrieval, comparison, and evidence bundling. Minor gaps exist, such as no explicit tool for listing supported product groups or metric catalogs, but agents can work around these using search and get.

Resources

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