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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuine behavioral context beyond that: which model generated the data, that only bets published pre-kick-off and then settled are included, and that individual bets are withheld as subscriber-only. It does not state a time window or refresh cadence, but the important disclosure is present.

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?

Purpose is front-loaded in the first clause, and the returned metrics are listed compactly. The two-sentence structure is efficient, though the parenthetical model distinction is slightly dense and could be tightened.

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, the description carries the job of describing returns and does so by naming the exact aggregates and their per-country segmentation, plus what is excluded. Remaining gap: it never states the period covered or how the record is scoped, which an agent might reasonably want for a track-record tool.

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?

Only one parameter (lang) with enum fr/en, a default, and 100% schema description coverage — the schema fully documents it. The description never mentions the language dimension or what it localizes ('summary and notices'), so it adds nothing beyond the schema. Baseline 3 per the high-coverage rule.

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 ('Returns Docteur Bet's public track record') and enumerates the exact contents: settled value bets, wins/losses, bet count, average odds and yield per country's bookmaker market. The clause distinguishing Docteur Bet's own model from 'the free Dixon-Coles model' clearly separates it from the sibling model/probability tools.

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 gives clear context for use (aggregate public performance of published value bets) and a scope boundary — individual bets are subscriber-only and not returned — so the agent knows not to expect raw bet listings here. It does not explicitly name alternatives or state when to prefer it over, e.g., get_season_projection, but the usage context is unambiguous.

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