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

Annotations already declare readOnlyHint/destructiveHint=false and openWorldHint=true, so safety is covered. The description adds genuine context beyond them by disclosing that data is 'stored' (not live) and that returns carry per-series units and observation dates, though it omits refresh cadence 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?

Three sentences, front-loaded with the coverage enumeration, then return format, then exclusions. Every sentence earns its place and there is no 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 zero-parameter, read-only tool with no output schema, the description adequately explains what is returned (per-series units and observation dates) and what is not included. It could still mention data freshness/update behavior, but nothing critical to invocation is missing.

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 tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. No parameter-level meaning is required here.

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?

States a specific verb (Inspect) and a well-scoped resource (stored US government macro observations), then enumerates the exact series covered. An agent can immediately tell this apart from siblings like get_futures_positioning or get_smart_money, which cover different data domains.

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?

Clear context is given via the negative scoping: 'No proprietary volatility, credit spreads, consumer surveys, stock breadth or AlphaYou regime score' and 'Not live economic releases or a forecast.' This tells the agent when NOT to use it, but it names no alternative tool to route to for the excluded data, leaving that to inference.

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