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SoapyRED

FreightUtils MCP Server

adr_lookup

Read-onlyIdempotent

Look up ADR 2025 dangerous-goods data by UN number, shipping name, or hazard class. Get hazard class, packing group, labels, and transport restrictions.

Instructions

Look up European road dangerous-goods (ADR 2025) reference data for a substance: hazard class, classification code, packing group, labels, special provisions, limited/excepted quantities, transport category, tunnel restriction code and Kemler (hazard identification) number. Covers 2,939 entries across all 9 hazard classes, from UNECE ADR 2025 (ECE/TRANS/352).

Provide exactly ONE of: un_number (exact lookup — returns every packing-group variant of that UN number), search (case-insensitive partial match on the proper shipping name), or hazard_class (all entries in a class or division). un_number is normalised — "1203", "UN1203" and "un 1203" are equivalent, and normalized_input reports the correction; explosives keep their leading zero ("0004").

Behavior: read-only reference lookup; name searches return up to 50 entries, class filters up to 100. An unknown UN number or a search with no hits errors with the API's NOT_FOUND body and a retry hint. Rate-limited (anonymous use: 25 requests/day per IP): a 429 error body carries retry_after_seconds and a Retry-After header — back off and retry, or call get_subscribe_link for higher limits.

Returns: count and results[] — per entry: un_number, proper_shipping_name, class, classification_code, packing_group, labels, special_provisions, limited_quantity, excepted_quantity, transport_category, tunnel_restriction_code, hazard_identification_number and variant_index/variant_count — under result, plus confidence, _source and citation (the FreightUtils v1 response envelope).

Limitations: a factual compilation of the ADR table, not legal or compliance advice; classification remains the consignor's responsibility — verify against the current UNECE ADR text.

Related: adr_lq_eq_check (checks quantities against the LQ/EQ values returned here), adr_exemption_calculator (1.1.3.6 small-load points), consignment_calculator (flags dangerous-goods lines by UN number).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNoCase-insensitive partial match on the proper shipping name (min 2 characters). Example: "acetone".
un_numberNoExact UN number — 4 digits, optionally "UN"-prefixed; explosives keep their leading zero. Examples: "1203", "UN1203", "0004".
hazard_classNoAll entries in an ADR class or division. Examples: "3" (flammable liquids), "6.1" (toxic), "1.4" (an explosives division).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
resultYes
_sourceYes
citationYes
validityNo
warningsNo
confidenceYes
blocking_errorsNo
envelope_versionYes
normalized_inputNo
Behavior5/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds significant behavioral context: read-only nature, rate limits (25 req/day per IP), error handling (NOT_FOUND, 429 with retry), normalization of UN numbers, and max result caps (50 for search, 100 for class). No contradictions with annotations.

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 thorough and well-structured, with front-loaded purpose, then constraints, behavior, returns, limitations, and related tools. While slightly lengthy, every sentence adds essential information. A minor trim could improve conciseness, but it remains effective.

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?

Given the tool's complexity (3 parameters, output schema exists, annotations present), the description covers all necessary aspects: input options, output structure, error scenarios, rate limits, data source, limitations, and related tools. No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds valuable semantics beyond the schema: normalization rules for un_number ('UN1203' and 'un 1203' are equivalent), minimum length for search (2 characters), and examples for hazard_class ('3', '6.1', '1.4'). This helps the agent understand input formatting and constraints.

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's purpose: 'Look up European road dangerous-goods (ADR 2025) reference data for a substance' and lists all returned fields. It distinguishes from siblings by naming related tools and their functions (e.g., adr_lq_eq_check, adr_exemption_calculator).

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

Usage Guidelines5/5

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

Explicitly instructs to provide exactly one of three parameters (un_number, search, hazard_class) with detailed behaviors and examples. Also mentions limitations (not legal advice) and related tools for alternative use cases.

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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