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

With no annotations the description carries the full burden, and it delivers real behavioral context: evergreen URL stability, auto-resolution/roll-forward semantics, the two added response fields, and a payment requirement ($0.02 via x402). It does not explicitly state that it is read-only, but that is strongly implied by a probability lookup.

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?

The core content is front-loaded and useful, but the question 'Will major federal AI regulation become law this year?' is stated verbatim twice in consecutive sentences, which is pure repetition. Trimming that duplication would tighten the definition without losing 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-parameter read tool with no output schema and no annotations, the definition covers the essentials: what it returns conceptually, the evergreen roll-forward behavior, and the two non-standard response fields. A note on latency/freshness or confirmation of read-only status would make it fully complete.

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 and the schema is closed (additionalProperties: false), so there is nothing for the description to clarify. Baseline 4 applies.

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' for a named binary question) and immediately clarifies the evergreen convention that separates it from the plain probability_get / probability_search siblings. An agent knows it is fetching a rolling market instance rather than a fixed one.

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 evergreen mechanics ('auto-resolves to the current market instance and rolls forward') imply the use case — you call this for a recurring question when you always want the next occurrence — but there is no explicit when-to-use/when-not guidance or named alternative (e.g., probability_get for one specific instance). Usage is inferable, not stated.

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