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

With no annotations, the description carries the burden and does well: it discloses the evergreen/roll-forward behavior, the extra response fields (current_slug, rolls_to_next_at), the HTTP method, and the price. It omits any auth or rate-limit context, keeping it out of 5 territory.

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 question and the evergreen mechanic, then the response/BEHAVIOR details, then transport metadata. Slightly repetitive in restating the question, but every sentence carries information.

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, the description covers what it returns and how the evergreen rolling works, plus cost and docs. Nothing needed 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, which is the baseline-4 case; there are no argument semantics to explain. The description confirms there is nothing to supply (the URL is self-contained).

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 ('Live probability: What wins Best Picture at the next Oscars?') and distinguishes itself from the many sibling probability_evergreen_* tools by naming the exact recurring question. An agent can route to it without opening any schema.

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 usage model clearly: a stable URL that auto-resolves to the current market and rolls forward on resolution, so the answer is always about the next occurrence. It does not explicitly name alternatives like probability_get or probability_search, so it stops short of a 5.

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