As-of customs-tariff lookup: which measures/duty rates apply to a goods code in a jurisdiction (eu, us, uk, jp), optionally from an origin, on a date. Duty expressions are verbatim; every measure carries its legal citation. Not legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | goods code, 2-10 digits (HS/TARIC/HTS) | |
| date | No | as-of date YYYY-MM-DD, default today | |
| origin | No | ISO2 origin country (eu/uk only) | |
| direction | No | import | |
| jurisdiction | No | eu |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It adds meaningful detail: duty expressions are verbatim, every measure includes a legal citation, and the tool is not legal advice. It does not cover pagination, errors, or rate limits, but the core lookup behavior and output traits are transparently stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two tight sentences with no filler. The first sentence front-loads the purpose and query parameters; the second adds output behavior and a caveat. Every element earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and no annotations, the description covers purpose, query parameters, output characteristics, and a caveat. Minor gaps remain: direction/export semantics are not explained, and defaults are left to the schema. Overall, it is largely complete for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description restates key parameters (code, jurisdiction, origin, date) and enumerates jurisdiction values, matching the schema. Schema coverage is 60%, and the description helps clarify the query dimensions, but it does not mention the 'direction' parameter or its import/export semantics, leaving part of the parameter space underspecified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('lookup'), a clear resource ('customs-tariff'), and the core query dimensions: goods code, jurisdiction, optional origin, and date. It distinguishes this from the generic sibling tools (get_table, resolve_data, search_datasets) by its domain and purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when tariff/duty-rate information is needed as of a certain date) but provides no explicit guidance about alternatives or when not to use it. Sibling tools are not mentioned, so the decision boundary is left entirely to the agent.
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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Each tool targets a distinct operation: catalog search, exact table retrieval, natural-language data resolution, and tariff lookup. Even though search and resolve both accept English queries, one returns dataset IDs and the other returns observations, so the boundaries are clear.
Three tools follow a clear verb_noun pattern (get_table, resolve_data, search_datasets), and all names use lowercase snake_case. tariff_lookup deviates slightly from the verb-first pattern but remains consistent in style and readable.
Four tools is a well-scoped count for a read-only statistical data server. Each tool provides a distinct core capability with no redundant or filler tools.
The core read-only data workflow is covered: discover dataset IDs, fetch a known table, ask a data question, and run tariff lookups. Minor gaps such as schema-only metadata access or generic table filtering are not present, but agents can work around them with the existing tools.