bea_gdp_by_industry

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

Annotations already declare this a read-only, idempotent operation, so the bar is lower for behavioral disclosure. The description adds meaningful defaults (default table 1, industry ALL, default year range) and clarifies that 'Industry='ALL' returns all sectors,' which goes beyond what the schema states.

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 compact and front-loaded, with the primary action in the first sentence and the table/industry options in an easy-to-scan list. No redundant elaboration or filler.

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?

For a read-only query tool with four optional parameters and no output schema, the description covers the key choices: table ID, industry scope, and national focus. It omits output shape/pagination, but the annotations plus thorough schema make the missing information minor.

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?

Schema description coverage is 100%, so the baseline is 3; the schema already explains year, industry examples, table_id values, and frequency. The description adds minor value by listing TableID options and noting that ALL returns all sectors, but largely restates schema content.

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 opens with a specific verb and resource: 'Get GDP contribution by industry sector nationally from BEA GDPbyIndustry dataset.' This clearly differentiates the tool from nearby BEA siblings like bea_gdp_national (national totals) and bea_gdp_by_state (state-level data).

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 scope is clear: national-level GDP by industry. However, the description never names alternatives such as bea_gdp_by_state or bea_gdp_national or states when to prefer one over another. Usage context is implied by the tool's scope but not explicitly enforced.

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