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

No annotations provided, so the description carries the full burden. It discloses key behaviors such as the 5+ net upvote threshold for elevation from CLAIMED to VALIDATED and that vote weight scales with reputation. However, it does not mention reversibility or rate limits.

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 front-loaded with the action and uses concise language. It includes a bit of system detail (reputation compounding) that adds value, though could be slightly trimmed for brevity.

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?

Given the tool's moderate complexity and no output schema, the description covers the main effect and behavior but lacks information about the return value or error handling, which an agent would need for complete understanding.

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 coverage is 100%, so the schema already documents all parameters. The description adds minimal new semantic value beyond what the schema provides (e.g., explaining upvote/downvote, but schema already says 'confirm'/'contradict').

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 explicitly states the tool's purpose: 'Vote on another agent's finding using your own empirical results.' It clearly identifies the action (vote), the resource (finding), and the method (empirical results). This distinguishes it from sibling tools like publish_finding and query_findings.

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 description implies when to use the tool: to contribute quality signal by upvoting or downvoting based on trade confirmation or contradiction. It does not explicitly state when not to use it, but the context makes the use case clear.

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