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

Behavioral traits are richly disclosed beyond the annotations: percents are 'INTENSIVE shares: avg/min/max over a scope, never summed,' suppressed cells are 'null values with their verbatim letter — never zero,' and cross-edition comparisons carry an 'edition_scope note.' It also warns that 'Capex sums UNDERSHOOT below the published totals wherever suppression bites' and notes the product is experimental. No contradiction with readOnlyHint/idempotentHint.

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

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

The description is lengthy but well-structured, with a clear opening sentence, parameter examples, and a CAPITALIZED 'ENGRAVED BOUNDARY' section for caveats. It is slightly redundant in contrasting with the import data (mentioned twice), but overall every sentence adds substantive value. The front-loading of the purpose makes it easy to skim.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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 fact that the input schema is just a free-form object, the description is exceptionally complete. It covers source data, metrics, filtering/grouping dimensions, edition differences, suppression handling, and return format ('JSON aggregates with citations'). It even points to get_source_evidence_v1 for re-verification, providing full contextual closure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides no property definitions or descriptions (coverage 0%), so the description carries the full burden. It thoroughly explains all filter parameters (edition, table, data_year, naics_code, state, plant_size, geo_area_name), valid values for several (e.g., edition values 'asm_2018_2021' and 'ec_2022'), and the exact table names. It also clarifies that parameters must be passed as flat top-level keys and provides two concrete examples showing usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Query the verified share of US manufacturing plants USING industrial robots — plus workers exposed and robotics capex — from the Census Industrial Robotic Equipment product.' It names the specific resource and metrics, and distinguishes itself from sibling query_robotics_trade_v1 by contrasting 'INSTALLED-BASE reading the import data cannot see' and later referencing the import unit-count series.

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

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

The description explicitly says when to use the tool: 'Use this for "are factories actually adopting robots" questions.' It also points to the alternative tool for a different use case: 'the import unit-count series is query_robotics_trade_v1.' Additionally, it provides critical usage boundaries, such as 'the two editions are NEVER spliced into one trend' and 'Manufacturing plants only,' giving clear guidance on when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.