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

With no annotations, the description carries the burden and mostly does: it discloses the auto-resolution/roll-forward behavior, the extra response fields (current_slug, rolls_to_next_at), the HTTP endpoint, and the $0.02 x402 price. It does not discuss auth mechanics for x402 or rate limits, so it falls short of fully transparent.

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 and generally tight, but the question 'Who wins the next World Series?' is stated twice in the first two sentences, which is avoidable repetition in a short description.

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-param, no-output-schema tool with no annotations, the description covers what matters: the evergreen semantics, the additional response fields, the endpoint, and the price. Little an agent needs in order to call it correctly is missing.

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 the baseline is 4; there is no parameter surface for the description to clarify or obscure.

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 (live probability for the next World Series winner) and frames it as a stable evergreen endpoint. The 'evergreen' mechanism is explained well enough to distinguish it from the instance-based probability_get, though no sibling is named explicitly.

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?

Explains the evergreen contract clearly: auto-resolves to the current market and rolls forward on resolution, so the answer is always about the next occurrence. That gives the agent a clear condition for choosing this over a fixed-slug lookup, but it names no alternative tool and states no exclusions.

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