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FreightUtils MCP Server

by io.github.SoapyRED

Resolve Freight Reference

resolve_reference
Read-onlyIdempotent

Resolve an arbitrary freight identifier — one opaque string in, typed and cited candidates out. The agent front door: when you hold an identifier-ish token ("176", "UN1845", "NLRTM", "FOB", "22G1", "MSKU1100810", "D/E") and do not know which lookup tool fits, call this FIRST and follow the candidate's api_url / canonical_url (or the matching sibling tool) for depth.

Provide q: ONE identifier (single token, max 32 chars). Thirteen grammars all run — UN numbers, AWB prefixes, airline IATA/ICAO, airport IATA/ICAO, UN/LOCODE, ISO 6346 container numbers (check digit computed), HS codes (6-10 digits; national lines resolve at their 6-digit international parent), Incoterms, ADR tunnel codes, ULD serials, ISO container size/type codes. Ambiguity is the product: colliding grammars return MULTIPLE ranked candidates ("LHR" is Heathrow AND an Egyptian carrier's ICAO), never a silent guess.

Behavior: deterministic — normalize (trim, uppercase, collapse spaces/dashes, strip a UN prefix), match ALL grammars, rank by record-hit > verification status > static dataset prior (airlines/adr/unlocode first) > stable tiebreak; every candidate carries rank_basis so the ordering is inspectable. Zero candidates is a VALID result (count 0 + note), never an error. A failed container check digit returns a candidate with valid:false rather than dropping it. 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: query, normalized, count and candidates[] — each with entity_type, identifier_type, value_normalized, summary, record (the core fields the matching dataset holds), verification_status + verification_basis, rank_basis, canonical_url, api_url and a per-candidate dataset citation — under result, plus confidence, _source and citation (the FreightUtils v1 response envelope).

Limitations: v1 is SINGLE-TOKEN resolution, not free-text extraction — pass one identifier, not a manifest line; SCAC/BIC codes are not yet covered; a match asserts the identifier is known/well-formed, not that a shipment exists.

Related: every lookup tool this resolves into — adr_lookup, airline_lookup, airport_lookup, unlocode_lookup, hs_code_lookup, incoterms_lookup, container_lookup, uld_lookup, validate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesOne freight identifier — e.g. "176", "UN1845", "NLRTM", "FOB", "LHR", "22G1", "MSKU1100810", "090111", "AKE12345AB", "D/E".

Output Schema

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

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses extensive behavior: normalization steps, matching all grammars, ranking logic (record-hit > verification status > static dataset priority), handling of ambiguous, empty, and invalid-check-digit cases, plus rate-limiting details (429 with retry_after_seconds and Retry-After header). This goes well beyond the annotations and gives a complete behavioral contract.

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 thorough but verbose, repeating similar phrases in multiple sections (e.g., 'one opaque string in, typed and cited candidates out' appears in the purpose, provide, and returns sections). While structured with clear headings, it could be condensed without losing essential 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?

The description covers the return envelope (query, normalized, count, candidates with all subfields), error handling (rate limits, invalid check digits), limitations (single-token only, no SCAC/BIC), and references related tools, making it self-sufficient for an agent to decide and invoke 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 only parameter q is fully described in the schema with examples and constraints, and the description reinforces the expected format with a list of identifiers. While the schema already covers the semantics, the description adds a concise 'one freight identifier' clarification, so it earns a 4 rather than the 3 baseline.

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 resolves a single opaque freight identifier into typed and cited candidates, explicitly positioning it as the 'agent front door' and distinguishing it from the many sibling lookup tools by instructing to call it first when unsure which lookup fits.

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?

It provides explicit when-to-use guidance ('call this FIRST'), explains how to proceed with the returned candidate's api_url or canonical_url for depth, and lists related tools for reference, leaving no ambiguity about appropriate invocation.

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

A4.3/5.0
Disambiguation3/5

The tools cluster into clear domains and the descriptions are detailed, but there are several overlapping pairs: consignment_calculator vs shipment_summary, cbm_calculator vs unit_converter, and validate vs resolve_reference all have fuzzy boundaries. An agent could plausibly mis-select between the composite calculators or between identifier-handling tools despite the helpful cross-references.

Naming Consistency4/5

Naming is mostly consistent: reference tools use *_lookup, calculation tools use *_calculator, and checks use *_check. Exceptions like validate, resolve_reference, nearest_airport, and shipment_summary break the dominant pattern slightly, but all names are snake_case and generally predictable.

Tool Count3/5

At 25 tools, this sits at the heavy end of the range for an MCP server. The broad freight/logistics scope justifies many of them, but several single-purpose calculators could have been consolidated or grouped, making the surface feel larger and more redundant than necessary.

Completeness4/5

The server covers ADR dangerous-goods rules, multi-modal freight calculations, equipment lookups, identifier validation, customs/trade data, and emissions estimation. Minor gaps exist—no routing/geocoding, no SCAC/BIC coverage, no customs filing—but core freight-reference and calculation workflows are well supported.

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