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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable context: source-aware freshness, per-venue health at a specific endpoint, volume reporting methodology, real-money-only monetary totals, self-computed aggregates requiring citation, and no API key required. No contradiction 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 concise (4 sentences) and front-loaded with the most important information: the type of statistics and venues. Every sentence adds meaningful detail without unnecessary fluff.

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 has one optional parameter, a rich output schema, and comprehensive annotations, the description covers all necessary aspects: data aggregation, health monitoring, volume methodology, citation policy, and access requirements. It is fully self-contained for an AI agent to understand usage.

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 single optional parameter 'fiat' is well-described in the schema with default currency code. The description adds minimal additional value beyond the schema (only mentioning monetary totals cover real-money venues). Schema coverage is 100%, so baseline of 3 is appropriate.

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 states the tool provides aggregated prediction-market statistics across 12 venues, listing specific metrics (total/open/closed counts, volume, liquidity, highlights). It also distinguishes from pm_data_event for deeper event evidence, making the purpose precise.

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 directs users to pm_data_event for full event evidence and mentions the health endpoint for per-venue freshness. It also covers volume basis and citation requirements, though it doesn't enumerate all contexts where alternative tools should be used.

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