osint

Query AI Infrastructure Trade (chip imports)

query_ai_infrastructure_trade_v1

Read-onlyIdempotent

Query verified U.S. monthly IMPORTS of integrated circuits (HS-8542) — customs value (USD) by country of origin — from the U.S. Census Bureau's International Trade data.

Use this for "how much $ of chips did the US import (from Taiwan / South Korea / in total) and how is it trending" questions. HS-8542 is ALL integrated circuits (processors, memory, amplifiers, parts) — NOT AI-accelerator / GPU-specific. Filter by country (the verbatim Census name, e.g. "TAIWAN", "KOREA, SOUTH"), cty_code (the Census country code, e.g. "5830"), country_level ("total" = the all-countries TOTAL, "country" = an individual country, "grouping" = a Census bloc/continent like ASIA / APEC / EU), year, data_month (one month, ISO first-of-month e.g. "2026-04-01") or the data_month_from/data_month_to range. Group by any of country, cty_code, country_level, data_month, or year. Pass each parameter as a top-level key of params (flat — not nested under a filter, filters, or where key). Example: {"country_level": "country", "group_by": ["country"], "order_by": "general_value_usd", "top_n": 5} for the top source countries; {"country_level": "total", "group_by": ["data_month"]} for the national trend. Returns JSON aggregates with citations and optional row-level records when include_records is true — every value cites the exact Census response row, re-verifiable via get_source_evidence_v1.

Measures: general_value_usd (general imports value) and consumption_value_usd (imports for consumption) — value only; HS-8542 reports no meaningful quantity at this level, so there is no chip count. NEVER SUM across country rows: Census's groupings (ASIA, APEC, EU, OECD, ASEAN, the continents) OVERLAP each other and the individual countries, and the all-countries TOTAL contains everything — so adding rows double-counts. Filter country_level=total for the U.S. national figure, country_level=country for individual countries, or group_by country for the per-country series; a cross-row sum returns a country_aggregation note and nulls the metric in ranking remainders. Country is the country of ORIGIN (Census attribution), not where a chip is installed — there is no U.S. state/county breakdown. Imports only (not exports), customs value (not landed/CIF/duty), and recent months are preliminary and revised in later releases.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A5/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses return format ('JSON aggregates with citations and optional row-level records'), the double-counting pitfall ('NEVER SUM across country rows'), the meaning of country as origin rather than installation, that imports are not exports, customs value not landed/CIF/duty, and that recent months are preliminary. This is exemplary behavioral context.

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?

Despite being lengthy, the description is well-structured: purpose, usage examples, parameter details, measures, and critical caveats are separated logically. Every sentence adds necessary information, especially given the zero-coverage schema. No filler or redundancy.

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 is complex, but the description covers all dimensions: what data is returned, how to filter/group/order, which measures exist, why summing is wrong, the absence of quantity data, and citation/verifiability. With an output schema present and this rich description, an agent can confidently select and invoke the tool without guessing.

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?

The input schema is a generic `params` object with additionalProperties true, so schema coverage is 0%. The description compensates fully by enumerating all meaningful parameters (`country`, `cty_code`, `country_level`, `year`, `data_month`, `data_month_from/to`, `group_by`, `order_by`, `top_n`, `include_records`), specifying formats and allowed values, and stressing that parameters must be flat top-level keys. This is far more useful than the schema alone.

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 precise statement: 'Query verified U.S. monthly IMPORTS of integrated circuits (HS-8542) — customs value (USD) by country of origin — from the U.S. Census Bureau's International Trade data.' This names the exact verb, resource, and scope. It also differentiates from siblings by clarifying that HS-8542 covers ALL integrated circuits and is NOT AI-accelerator/GPU-specific.

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 states 'Use this for "how much $ of chips did the US import... and how is it trending" questions' and provides two complete example invocations for top source countries and national trend. It also gives a clear when-not-to-use signal: 'NOT AI-accelerator / GPU-specific.' This goes far beyond vague applicability.

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

A4/5.0

Disambiguation4/5

Each capability has a clearly named describe/query pair, and domains like power, AI infrastructure, robotics, and space are distinct. The generic describe_capability_v1/query_capability_v1 could be confused with the named variants, and the many ISO-specific interconnection queues share similar names, but descriptions explicitly disambiguate them.

Naming Consistency5/5

All tools follow a consistent lowercase snake_case verb_noun pattern: describe_<capability>_v1 and query_<capability>_v1, with a few utility tools like list_capabilities_v1 and get_source_evidence_v1. There is no mixing of conventions.

Tool Count2/5

At 62 tools, the set is far too large for typical server scope. The describe/query pairs inflate the count even though each is justified, and the generic capabilities plus per-ISO variants make it feel heavy and hard to navigate.

Completeness4/5

The server covers each domain thoroughly with describe, query, and evidence verification, plus generic fallbacks for capabilities not yet in the client's tool list. Minor gaps exist (e.g., no county/state attribution for some data, no load types in ISO queues) but they are explicitly documented and workable.

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