ldbd_get_macro_indicators

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

With no annotations provided, the description fully discloses behavior: return fields (latest value, prior value, change, trend, as_of, frequency, nature tag, source), the meaning of 'nature' tags, the data-only nature, and the absence of interpretation. It also notes public access. This is comprehensive and exceeds typical transparency.

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 a dense single paragraph, but every sentence adds relevant information. It is somewhat long, but the complexity of the tool justifies the length. It front-loads the core purpose and then layers details logically, though bullets or structured breaks could improve scannability.

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 one optional parameter and no output schema, the description is remarkably complete: it explains the dashboard contents, return values, field semantics, sources, access requirements, and the data-only philosophy. There are no significant gaps for an agent to invoke the tool 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 schema covers the single 'category' parameter with an enum and description, but the description adds significant meaning by detailing what each category contains (e.g., 'rates & yields (Treasury yields, yield-curve spreads, fed funds)'). This goes well beyond the bare enum values and helps the agent select the correct filter.

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 with a specific verb ('Get') and resource ('macro-economic indicator dashboard grouped by category'), and enumerates the categories and indicator types. It is differentiated from siblings by domain (macro vs chart indicators), but does not explicitly name alternatives or contrast with the sibling tool ldbd_get_chart_indicators.

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 clear context: it returns data only, is public, requires no API key, and the optional category filter is explained. It implies the agent should use this for raw macro data and perform its own interpretation. However, it does not explicitly state when to use this tool versus alternatives or provide exclusionary guidance.

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