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

Annotations already cover the safety profile (readOnly, idempotent, closed-world, non-destructive), and the description adds real context beyond them: 30-minute self-caching, all values are percentages, and index-point fair values, index level, and earnings inputs are deliberately withheld. That last disclosure usefully prevents an agent from asking for data that will never be returned.

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?

Front-loaded with the verdict and the model, then the return fields — appropriate ordering. It is one very long compound sentence, though, and the five-band enumeration makes it dense to parse; splitting into two or three sentences would cost nothing and read faster.

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?

With no output schema and no parameters, the description carries the full burden of describing return values and does so thoroughly: named fields, units, directional semantics ('lower means richer', '+'/'-' distance), and what is intentionally excluded. An agent knows exactly what it will receive and how to interpret it.

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?

Zero parameters, so the baseline of 4 applies; the description explicitly confirms 'No parameters,' removing any doubt about needing inputs. Nothing further is required.

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 resource (S&P 500 valuation verdict) and the exact model behind it (discounted-earnings / equity-risk-premium), then enumerates the concrete outputs: ladder verdict, implied ERP, zone, distance from fair value, and five ERP bands. An agent can tell immediately this is a market-level valuation read, distinct from the macro/liquidity/breadth/gappers siblings.

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?

There is no statement of when to call this versus get_cross_asset_regime, get_macro_liquidity, or get_market_breadth, and no prerequisites or exclusions. The only interpretive cue is 'A model, not a forecast,' which frames how to read the output rather than when to select the tool.

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