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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the description earns credit for adding provenance and limits instead of repeating them: trained on stored Sportmonks results, metrics returned, the 50-published-match floor for paired rates, and an explicit no-confidence-guarantee disclaimer. Only minor gaps remain, such as whether results are computed live or cached.

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?

Four tight sentences, front-loaded with what the tool is and what it returns, followed by the availability threshold and the expectation-setting disclaimer. Each sentence carries information; the closing caveat is arguably padding but does real work in preventing over-trust in the numbers.

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 must carry the return-value burden, and it does: sample sizes, date windows, accuracy, Brier score, log loss, and paired-rate availability. An agent knows what a call yields and when data may be thin. Only the response envelope shape remains unspecified.

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. The description correctly implies a single, unfiltered global benchmark rather than a per-match or per-league query.

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 and resource: returns a chronological holdout benchmark of an experimental football outcome model plus comparisons against National expert groups on the same matches. The scope is concrete enough that an agent can distinguish it from get_accuracy or get_consensus, though it never names those siblings to sharpen the boundary.

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 explicit when-to-use or when-not-to-use guidance, and no sibling is named as an alternative. The only routing signal is implicit: the tool compares the model against expert groups, so it is presumably the choice when a model-vs-experts readout is wanted. An agent must infer that from the purpose statement alone.

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