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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, establishing it as a safe read operation. The description adds meaningful behavioral context beyond this: the score is 'a single 0 to 100 reading' that measures present valuation against history, that it is 'not a forecast and not a market timing signal.' This disclosure of what the tool does NOT do adds genuine transparency value.

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 tight paragraphs: what it returns, when to use it, and what it is not. Every sentence earns its place—the methodology sentence names the four indicators compactly, and the exclusions and disclaimers are each single sentences. No wasted words.

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, no-output-schema read-only tool, the description is complete: it defines the output (0-100 score), names its components, states scope/exclusions, and disclaims forecasting/timing. Combined with generous annotations (readOnly, idempotent, non-destructive), there's nothing materially missing for an agent to select and invoke this 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 tool has zero parameters, so there is no schema burden to compensate. Per the rubric, 0 params gives a baseline of 4. The description explains what the returned reading represents, which is the relevant semantic content given there's no input to parameterize.

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 returns the Zyberno Market Valuation Score, a specific 0-100 reading of US equity market expensiveness relative to history. It explicitly names the four constituent indicators (Buffett Indicator, Shiller PE, Treasury curve, Baa spread) and distinguishes it from individual company/sector/index tools like get_stock_valuation and list_flagged_stocks.

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 states when to use it ('questions about the market as a whole, overall valuation levels, whether US stocks broadly look expensive or cheap') and provides clear exclusions ('Do not use it for any individual company, sector, or index price level'). This maps well to the sibling set where get_stock_valuation, calculate_dcf_value, and screen_stocks handle narrower scopes.

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