bea_underlying_gdp_by_industry

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

The readOnlyHint annotation already signals a safe read operation, so the description adds valuable context beyond that: annual-only data, a 1997 start year, and a BEA caution about lower estimate quality. These are meaningful behavioral and data-quality disclosures not inferable from the annotation alone.

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 well-structured. The core purpose is front-loaded in the first sentence, followed by two short sentences on temporal scope and data quality, and a final action-oriented pointer for prerequisites. Every sentence earns its place without redundancy.

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, 3-parameter tool without an output schema, the description covers the necessary context: temporal availability, data quality caveat, and how to discover valid parameter values. It is nearly complete; a minor gap is not describing the response format or the fact that 'ALL' is the default for industry/year, though those are in the schema.

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 schema already documents all three parameters with clear descriptions (year, industry, table_id), achieving 100% coverage. The description repeats the tip to use bea_dataset_info for table_id/industry codes, which is already in the schema, so it adds no new parameter-level meaning. Baseline 3 is appropriate.

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 clear verb+resource structure: 'Get Underlying GDP by Industry.' It explicitly differentiates from the sibling tool by stating 'more industry detail than the main GDPbyIndustry dataset,' making the tool's unique scope immediately apparent.

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?

It provides context for when to prefer this tool (when more industry detail than the main dataset is needed) and directs users to bea_dataset_info for discovering valid TableIDs and Industry codes. It does not explicitly state when to choose the main GDPbyIndustry tool instead, but the comparison implies it, earning a 4 rather than a 5.

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