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

Annotations already declare readOnlyHint=true, so safety is covered. The description adds genuinely new behaviorally relevant context: the per-call cost ($0.01/call) and the recommended polling cadence, both of which affect how an agent should budget and schedule calls.

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-loads the core value proposition, then the cadence guidance, then cost — every sentence earns its place and nothing is padded. Dense enumeration of contents is scannable rather than bloated.

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?

There is no output schema, so the description must describe what comes back, and it does: venue statuses, next opens, ranked event pressure, top-severity events, and prediction-market probabilities. Combined with cost and cadence, an agent has enough to call it correctly.

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. The description correctly implies a parameterless, fire-and-forget snapshot, and there is nothing in the schema that needs compensating for.

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 and enumerates its contents (venue open status, event pressure, top-severity events, prediction-market probabilities), which is concrete and non-tautological. It doesn't explicitly name a sibling to contrast against, but 'one-call situational picture' versus the narrow siblings (impact_for, events_since) makes the breadth distinction inferable.

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?

Explicitly says when to call it — 'first in a session, or every few hours, to decide whether to look deeper' — giving clear cadence and intent. It stops short of naming alternatives or stating when not to use it (e.g. when a targeted sibling like impact_for is sufficient).

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