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

The description goes far beyond the read-only annotation by detailing internal behaviors: it explains the partition_check sum logic, the >=0.30 Jaccard similarity anchor, placeholder filtering (>20% returns null signal), and the fill check against live CLOB depth with advice not to trade when realizable_edge_pp <= 0. This provides rich context that the annotations alone do not.

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 lengthy but highly structured, with clear bolded labels (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and a front-loaded summary. Every section contributes essential operational detail, and the length is justified by the tool's complexity. There is no filler or repetition.

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 and lack of an output schema, the description is exceptionally complete. It describes the response shape (opportunities[], partition_check, fill check fields), handles edge cases (null signal, skipped_low_similarity, thin_legs), and provides actionable guidance on interpreting results. No critical gap remains for an agent to invoke this tool 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?

Although the input schema already provides 100% description coverage for both parameters, the tool description adds significant semantic value by giving concrete example slugs ('fed-decision-may-2026'), explaining the behavior triggered by each parameter, and clarifying that full URLs are also accepted. This exceeds the baseline for high schema coverage.

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 opens with a specific verb and resource: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly distinguishes this tool from siblings like polymarket_edges (likely focused on edges) and polymarket_fill_risk (pricing risk), making its purpose unmistakable.

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 provides explicit usage guidance: call with NO args for trending_scan, use 'event' for a specific market, and use 'topic' for cross-event scanning. It explains when each mode is appropriate and even references an alternative tool for custom sizing ('For custom sizing use polymarket_fill_risk'). This fully satisfies when-to-use/alternative guidance.

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