bea_nipa_underlying_detail

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

The annotation readOnlyHint=true already signals a safe read operation, and the description adds a valuable caution that the detailed estimates are lower quality than published aggregates. This is behavioral context beyond what annotations provide, though it doesn't discuss other behaviors like response size or rate limits.

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 core purpose, followed by a key caveat and practical examples. Every sentence contributes useful information (purpose, quality warning, common tables, discovery hint) with no filler or repetition of schema content.

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 has no output schema and only 3 optional parameters, the description covers the essential context: what the tool returns, quality caveats, how to choose a table, and where to find more tables. It doesn't detail the exact return structure, but the examples and caution make it sufficiently complete for an agent to invoke 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?

The input schema covers 100% of parameter descriptions, so the baseline is 3. The description adds semantic value by mapping common table IDs to their economic concepts (e.g., U20305 → PCE current $), which helps the agent understand the table_name parameter. It does not add detail for year or frequency, but the schema already covers those adequately.

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 uses a specific verb ('Get') and resource ('NIPA underlying detail data') and clarifies it provides 'more granular national account breakdowns.' This distinguishes it from aggregate-level BEA tools and gives concrete examples of common tables, removing ambiguity.

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 contrasts this tool with 'published aggregates' (implying use the other BEA tools for those) and directs users to bea_dataset_info for table discovery. It also provides a list of common tables with what they represent, giving clear guidance on when to use this tool and how to select parameters.

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