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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds crucial behavioral context: it is free public, no API key required, the orientation is human/aggregator-reviewed (never price-inferred), and it includes quality checks like requirePriced. No contradictions with annotations.

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 relatively long but well-structured: it starts with the core purpose, then details parameters and methodology, and ends with disclaimers. Every sentence adds value, though it could be slightly more concise for a quick scan. Still, it is well-organized and front-loaded.

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 complexity (9 parameters) and the presence of an output schema, the description is remarkably complete. It explains the output structure (e.g., deltaPoints, matchedOutcomeCount), the methodology behind delta calculations, and the quality floor. The agent has a thorough understanding to use this tool effectively.

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?

With 100% schema coverage, baseline is 3, but the description adds significant value by explaining parameters in context, such as sort options with their meanings (e.g., 'max_outcome_delta_desc avoids multi-leg basket noise'), and detailed explanation of requirePriced. This goes beyond the schema's descriptions, earning a 4.

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 clearly defines the tool as providing 'cross-venue disagreement clusters' for prediction markets, explaining that it shows events matched as the same question across venues. It distinguishes itself from a sibling tool (pm_quote) by stating that pm_quote is for tradability of one specific outcome, which helps the agent select the correct tool.

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 explicitly tells the agent when to use pm_quote instead (for tradability of one outcome) and advises caution on requirePriced (research/debug only). However, it does not cover when to use other sibling tools like pm_data_calibration or pm_data_event, limiting the differentiation.

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