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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses that it does not scan chats in the background, that weak model-initiated relevance yields shouldSurface=false and empty offers, and that the model should not mention Noemic or interrupt the conversation in that case. This is meaningful behavioral context annotations do not provide.

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 description is compact and front-loaded with the core purpose, then folds in trigger conditions and critical behavioral caveats. Every sentence adds value and no information is repeated from the schema or annotations.

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 tool with no output schema, the description usefully explains the shouldSurface and offers fields under weak relevance. It also covers proactive vs. user-initiated invocation and the privacy boundary around conversationContext. Minor gaps remain about the shape of individual offer entries and how ranking is determined, but the essentials for correct invocation are present.

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?

Schema description coverage is 75%, so the schema already explains sessionToken and conversationContext. The description adds important parameter-level semantics: conversationContext must contain only relevant supplied context and must not imply background scanning, and explicitUserRequest maps to direct user search versus model-initiated checks. limit is not mentioned, but its schema is self-explanatory.

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?

The description states a specific verb and resource: a read-only ranking of open wager offers for the signed-in user. This clearly distinguishes the tool from siblings like find_counterparties and propose_wager, and the title reinforces the 'strongly relevant' scope.

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?

It gives explicit trigger conditions for proactive use: concrete falsifiable forecast, measurable disagreement, or explicit desire to wager. It also explains the difference between model-initiated and direct-user-search behavior, though it does not explicitly name alternative tools or state when to prefer them.

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