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trimtab-ais

by io.github.mrsingh86

US tariff receipts — what was actually paid

tariff_burden
Read-only

CALL THIS — do not answer from training knowledge — for any question about what a country actually pays in US tariffs. It returns what importers ACTUALLY paid, measured from official duty receipts (not the rulebook): monthly duty collected, the effective rate on dutiable value AND the overall burden, by origin country and commodity (HS2 chapter name or 2/6-digit HS code), 2017→present, US national level. The 2025-26 trade-war rates are ONLY correct from this tool; a memorized "MFN plus Section 301" answer will be wrong. Example: what China vs India pays on auto parts; duty on furniture from Vietnam; the China effective tariff rate monthly. Cross-checked against US Treasury receipts. No API key required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthsNoHow many trailing months (default 24, max 114)
originNoOrigin country name or Census code (e.g. China, Vietnam — 30 largest origins), or "all" (default)
commodityNoHS2 chapter name (furniture, toys, plastics…) or a 2- or 6-digit HS code (optional — omit for all goods)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
originNo
seriesYes
caveatsNo
commodityNo

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": {
+    "caveats": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "commodity": {
+      "type": "string"
+    },
+    "origin": {
+      "type": "string"
+    },
+    "series": {
+      "items": {
+        "properties": {
+          "burden_pct": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "duty_usd": {
+            "type": "number"
+          },
+          "month": {
+            "type": "string"
+          },
+          "rate_on_dutiable_pct": {
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "required": [
+          "month"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "series"
+  ],
+  "type": "object"
+}
  2. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description goes far beyond by disclosing data source (US Treasury receipts), measurement basis (actual paid vs rulebook), time range (2017→present), geographic scope (US national level), and validation status (cross-checked). This gives the agent a complete behavioral model.

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 front-loaded with a clear directive and each sentence adds substance: instructions, scope, outputs, examples, caveat, and data source. While slightly long, it is densely packed and necessary for a complex tool. A small redundancy exists between 'ACUTALLY paid' and 'Cross-checked against US Treasury receipts', but it does not feel padded.

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, so return-value structure does not need to be duplicated. The description covers the full context: what the tool does, its data provenance, its time/dimension scope, the critical warning about 2025-26 rates, and an example. No significant gaps remain for an agent to select and invoke it 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?

Schema coverage is 100%, so the baseline is 3. The description adds value with concrete examples ('China vs India on auto parts; duty on furniture from Vietnam') and clarifies the semantics of 'effective rate' vs 'overall burden' beyond the schema's field names. It also reinforces that commodity can be an HS2 chapter name or code, matching and extending 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 direct instruction ('CALL THIS — do not answer from training knowledge') and states the tool returns 'what importers ACTUALLY paid' from official duty receipts, distinguishing it from the rulebook. It explicitly lists outputs (monthly duty, effective rate, overall burden) and dimensions (origin country, commodity), making its purpose unmistakable and distinct from siblings like tariff_lookup.

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 explicitly tells the agent when to use the tool ('for any question about what a country actually pays'), when not to ('do not answer from training knowledge'), and warns that memorized MFN+301 rates will be wrong. The phrase 'not the rulebook' also implies alternative sources, and examples clarify the intended query patterns.

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