osint

Query Robotics Trade (industrial-robot imports, value + robot counts)

query_robotics_trade_v1

Read-onlyIdempotent

Query verified U.S. monthly IMPORTS of INDUSTRIAL ROBOTS — customs value (USD) AND unit counts (number of robots) — by country of origin, from the U.S. Census Bureau's International Trade data.

Use this for "how many robots is the US importing, from whom, and what are they worth" questions — the only high-frequency official U.S. robotics series. Covers the nomenclature's two robot-specific HS-10 codes, served as the commodity dimension: "8479500000" (INDUSTRIAL ROBOTS, NESOI — multipurpose: welding/assembly arms, AMRs) and "8428700000" (INDUSTRIAL ROBOTS FOR LIFTING, HANDLING, LOADING OR UNLOADING — created by HS 2022; no data before 2022-01, a structural absence, never zero). Filter by commodity, country (the verbatim Census name, e.g. "JAPAN", "CHINA", "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 commodity, 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: {"commodity": "8479500000", "country_level": "country", "group_by": ["country"], "order_by": "general_quantity_units", "top_n": 5} for the top robot-supplying countries by unit count; {"country_level": "total", "group_by": ["data_month", "commodity"]} for the national trend per code. 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 / consumption_value_usd (customs value) and general_quantity_units / consumption_quantity_units (Census's "NO" unit of measure = the number of robots). 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 — adding rows double-counts; a cross-row sum returns a country_aggregation note and nulls the metrics in ranking remainders; filter country_level=total for the U.S. national figure. The two commodity codes ARE disjoint — adding them is legitimate — but a combined time series changes composition at 2022-01 (a commodity_scope note flags it). This is the import FLOW, not the installed base or operational stock of robots in U.S. factories; no maker, model, or humanoid breakdown (customs-classified); country is the country of ORIGIN, not which U.S. state or factory receives the robots; imports only (not exports); customs value (not landed/CIF/duty); 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

A4.7/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint), the description discloses critical behavioral traits: the overlapping country groupings that cause double-counting and the 'country_aggregation note' that nulls metrics, the structural absence of data before 2022-01 for one HS code, the preliminary/revision nature of recent months, and the country-of-origin semantics. It also clarify that the two commodity codes are disjoint and summable, while cross-country sums are not. This is rich behavioral context that annotations alone cannot provide.

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 every sentence earns its place, and the essential information is front-loaded in the first sentence. It uses paragraphs for distinct topics (main purpose, usage examples, measures, caveats) which aids scanning. It is slightly verbose, but given zero schema coverage, the length is justified. A more structured list format could improve scannability, so a 4 is appropriate.

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 description covers data source, metrics (general_value_usd, consumption_value_usd, general_quantity_units, consumption_quantity_units), filters, grouping, aggregation warnings, structural caveats, and return behavior (JSON aggregates with citations and optional records). Since an output schema already exists, it needn't detail return fields. This is fully complete for a complex query tool with no schema-defined parameter details, and leaves minimal gaps for the agent.

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?

With schema description coverage at 0%, the description fully compensates by listing and explaining all parameters: commodity, country, cty_code, country_level, year, data_month, data_month_from/to, group_by, order_by, top_n, include_records. It provides value examples ('JAPAN', 'KOREA, SOUTH', '2026-04-01'), clarifies flat param structure ('not nested under a filter... key'), and gives two concrete example parameter objects. This exceeds the baseline of 3 and fully equips the agent to construct calls.

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+resource statement: 'Query verified U.S. monthly IMPORTS of INDUSTRIAL ROBOTS — customs value (USD) AND unit counts (number of robots) — by country of origin, from the U.S. Census Bureau's International Trade data.' This clearly distinguishes the tool from siblings like query_robotics_adoption_v1 or describe_robotics_trade_v1 by focusing on import flows with explicit data source and metrics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states usage: 'Use this for "how many robots is the US importing, from whom, and what are they worth" questions' and provides detailed exclusions: 'This is the import FLOW, not the installed base or operational stock... imports only (not exports); customs value (not landed/CIF/duty).' While it doesn't name an alternative sibling for excluded cases, the context is clear and the 'only high-frequency official U.S. robotics series' framing implies uniqueness. Missing explicit alternative references prevents a 5.

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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