bea_underlying_gdp_by_industry

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

Annotations already declare readOnlyHint=true. The description adds behavioral context: 'Annual data only, starting from 1997' and 'BEA caution: quality of these detailed estimates is lower than published aggregates.' This goes beyond annotations, although return format or pagination are not described.

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 four sentences, each adding essential information: purpose, data constraints, quality note, and discovery guidance. No filler, well-structured.

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 simplicity (3 parameters, no output schema), the description covers purpose, sibling differentiation, data constraints, quality, and parameter discovery. It references a helper tool, making it complete for an agent to use correctly.

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 coverage is 100%, so baseline is 3. The description adds value by stating default table_id '210' for value added and directing users to bea_dataset_info for valid codes. It also clarifies the year parameter applies to annual data starting from 1997.

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 'Get Underlying GDP by Industry' and explicitly distinguishes it from the sibling 'bea_gdp_by_industry' by saying 'more industry detail than the main GDPbyIndustry dataset.' This provides a specific verb, resource, and scope.

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 provides explicit guidance: 'Annual data only, starting from 1997' and 'Use bea_dataset_info to discover valid TableIDs and Industry codes.' It also implies when to use (more detailed data) and not to use (lower quality, or when higher quality aggregates are needed) by contrasting with the main dataset.

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