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trimtab-ais

by io.github.mrsingh86

US import trade query

trade_query
Read-only

Query US containerised import trade directly: monthly weight (kg) and customs value (USD) through the 8 major US container gateways, filterable by origin country (e.g. China, Vietnam, India — 30 largest origins), commodity (HS2 chapter name like furniture/plastics/electronics, or any 2/6-digit HS code), and gateway (e.g. us_la_longbeach, savannah). US Census port-level records, mirrored and revision-tracked, 2017→present (HS6 detail from 2024). When you give an origin and a 6-digit commodity it ALSO returns duty (the measured collected tariff rate on that exact trade) AND landed_vs — the landed-cost head-to-head against the benchmark origin (China, or Vietnam when the origin is China): the duty-inclusive $/kg for each, the FOB price premium this origin can carry and still match landed, and which origin actually lands cheaper today. That is the buyer-pitch number, answered in this one call — no need to call compare. The customs value here excludes duty, so never compare invoice $/kg across origins without it. Ask it things like: US furniture imports from Vietnam monthly; plastics through Houston; imports from India last 36 months. No API key required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthsNoHow many trailing months (default 24, max 60)
originNoOrigin country name or Census Schedule C code (optional — omit for all origins)
gatewayNoGateway zone or name: us_la_longbeach, us_ny_nj, us_savannah, us_houston, us_charleston, us_norfolk, us_oakland, us_seattle_tacoma (optional)
commodityNoHS2 chapter name (furniture, plastics, electronics, machinery, toys…) or a 2- or 6-digit HS code (optional)
share_draftNoSet true to also receive a ready-to-share social post template for this answer (optional; default false)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
answerNo
seriesYes
caveatsNo

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "answer": {
+      "type": "string"
+    },
+    "caveats": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "query": {
+      "type": "object"
+    },
+    "series": {
+      "items": {
+        "properties": {
+          "month": {
+            "type": "string"
+          },
+          "usd_per_kg": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "value_usd": {
+            "type": "number"
+          },
+          "weight_kg": {
+            "type": "number"
+          }
+        },
+        "required": [
+          "month",
+          "weight_kg",
+          "value_usd"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "query",
+    "series"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / properties / share_draft
      Added value: +{
+  "description": "Set true to also receive a ready-to-share social post template for this answer (optional; default false)",
+  "type": "boolean"
+}
  3. Added

TDQS

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description adds rich behavioral context: data source (US Census port-level records), time range (2017→present), revision tracking, HS6 detail from 2024, the exclusion of duty from customs value, and the automatic computation of duty and landed_vs when origin and 6-digit commodity are provided. This goes far beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every sentence earns its place. It front-loads the core purpose, then systematically covers filters, data quality, computed outputs, usage examples, and a warning. There is no redundant filler or repetition of schema details.

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 and the presence of an output schema, the description is fully complete. It covers data provenance, time coverage, parameter nuances, special computed outputs, usage examples, and the absence of an API key. No gaps remain for the agent to infer.

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?

Although schema coverage is 100%, the description adds crucial context to each parameter: origin accepts country names or Schedule C codes, commodity can be HS2 chapter names or HS codes, gateway examples are given, and months default/max are clarified. It also explains how parameters interact (origin+6-digit commodity triggers duty and landed_vs). This meaningfully enriches 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 opens with a specific verb ('Query') and clearly identifies the resource: US containerised import trade with monthly weight and customs value via 8 gateways. It explicitly differentiates itself from the sibling tool 'compare' by stating 'no need to call compare', making its unique scope unmistakable.

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?

Provides explicit when-to-use guidance with concrete examples ('Ask it things like: US furniture imports from Vietnam monthly...'), an exclusion ('no need to call compare'), and a critical caution ('never compare invoice $/kg across origins without it'). This clearly signals when the tool should be selected over alternatives.

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

A3.9/5.0
Disambiguation2/5

Several tools overlap significantly in the tariff/trade domain: tariff_story explicitly replaces hs_search, tariff_lookup, and tariff_burden, and trade_query also returns duty data. This creates ambiguity about which tool to invoke for a given tariff question, though non-tariff tools (compare, event_studies, month_in_review) are clearly distinct.

Naming Consistency2/5

Tool names follow mixed conventions: some are verb-first (compare, query_series, tariff_lookup), others are noun-first (commodity_profile, tariff_burden, event_studies), and there's no consistent prefix or verb pattern. While readable, the lack of a unified naming scheme makes the set feel less coherent.

Tool Count4/5

With 13 tools, the set is within a reasonable range for a trade data service and covers most query needs. It's slightly larger than necessary given the overlapping tariff tools, but not excessive.

Completeness4/5

The surface covers tariff lookup, actual duties paid, trade volumes, comparisons, profiles, event studies, and rulings search, which is comprehensive for a read-only trade data resource. Minor gaps exist (e.g., no tool for gateway-specific tariff burden, and the meta-tool ledger_meta hides a set of archived tools), but core workflows are well supported.

Resources