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

Annotations already cover the safety profile (readOnly, closed-world, non-destructive), so the description's added value is data-scope semantics: it discloses that aggregates come only from fully resolved books, that grading is cap-weighted on long picks, and that the 69 pre-published_rule_from backtest books follow a different rule and are segregated into backtest_ fields. That is genuinely useful context an agent cannot infer from annotations. It stops just short of 5 because the rule-versioning sentence is dense and partly re-describes output fields.

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 core 'what it does' statement, which is good, but the middle sentence strings together hit-rate rule scope, the 69-book cohort, and the backtest_ field names in one hard-to-parse clause. Some of that field-level detail is redundant given an output schema exists.

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 aggregate tool with annotations and an output schema, the description covers data inclusion criteria, grading methodology, rule versions, and sibling routing. The remaining gap is minor: no indication of the time horizon or coverage of the aggregates beyond 'fully resolved books'.

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; baseline is 4. No misleading parameter claims are made.

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+resource (get aggregates over fully resolved books) and enumerates the aggregate families: forecast counts, returns, hit rates, and beat/flat/trail SPY verdicts. It also distinguishes scope from siblings, calling out get_calibration and get_book_portfolio by name.

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?

Explicitly routes the agent: 'Use this for overall performance, get_calibration to test the ranking itself, and get_book_portfolio for a single book.' The alternative-selection condition is stated directly rather than implied.

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