osint

Query AI Infrastructure Equipment Trade (chip-making tools)

query_ai_infrastructure_equipment_trade_v1

Read-onlyIdempotent

Query verified U.S. monthly IMPORTS of semiconductor-manufacturing EQUIPMENT (HS-8486) — customs value (USD) by country of origin — from the U.S. Census Bureau's International Trade data.

Use this for "is the fab buildout actually tooling up, and who supplies the machines" questions — the equipment leg of the fab lifecycle: construction spending (ai_infrastructure.construction) measures the shell, this measures the tools flowing in, and chip imports (ai_infrastructure.trade) measure the output side. HS-8486 covers machines and apparatus used solely or principally to MANUFACTURE semiconductor boules/wafers, devices, and integrated circuits — AND flat-panel displays (Census does not split them at this level); it is NOT the chips themselves (those are HS-8542). Filter by country (the verbatim Census name, e.g. "JAPAN", "NETHERLANDS", "KOREA, SOUTH"), cty_code (the Census country code), 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 tool-supplying 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; no tool counts, and no tool-type or vendor breakdown (one HS4 heading: no lithography-vs-deposition-vs-etch split, no per-manufacturer series such as ASML). 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; a cross-row sum returns a country_aggregation note and nulls the metric in ranking remainders. Filter country_level=total for the U.S. national figure. Country is the country of ORIGIN (Census attribution), not which U.S. fab, state, or operator receives the equipment — there is no U.S. place breakdown. Imports only (not exports), customs value (not landed/CIF/duty), and recent months are preliminary and revised in later Census releases.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses the return format (JSON aggregates with citations, optional row-level records), the exact behavior when summing overlapping rows (returns a country_aggregation note and nulls metrics), the fact that values are customs value not CIF/duty, the preliminary nature of recent months, and the data's re-verifiability via get_source_evidence_v1. This is rich 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.

Conciseness4/5

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

The description is long but well-organized and front-loaded with a purpose sentence. Every section provides necessary detail given the tool's complexity, though there is a minor redundancy (e.g., mentioning the lack of vendor/tool-type breakdown in two places). It could be slightly tightened but remains efficient.

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, empty parameter schema, and rich annotations, this description is fully self-contained. It covers parameter semantics, provides examples, explains overlapping-group double-counting risks, defines the available measures, and clarifies the data's scope (origin country, imports only, preliminary revisions). The agent has everything needed to invoke the tool correctly.

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 effectively an empty `params` object with 0% coverage and no property definitions. The description fully compensates by detailing all filter and grouping parameters (country, cty_code, country_level, year, data_month, ranges, group_by, order_by, top_n, include_records), explaining allowed values and giving concrete examples. It leaves no ambiguity.

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: it queries verified U.S. monthly imports of semiconductor-manufacturing equipment (HS-8486) with customs value by country of origin from Census data. It explicitly contrasts this with the construction and chip-import sibling tools, naming them and explaining the fab lifecycle roles. The tool name also clearly indicates this is the query variant.

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?

The description explicitly says 'Use this for... questions' and names the alternative tools (ai_infrastructure.construction, ai_infrastructure.trade) with a clear division of responsibilities. It also provides strong guidance on how to interpret results and warns against summing across country rows due to overlapping groupings, giving the agent clear context for when and how to use the tool.

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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