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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds extensive behavioral context: scans top ~200 markets, walks child markets, checks date-axis/threshold-axis ordering, computes partition_check, uses semantic anchor (Jaccard similarity ≥0.30), partition filter (drops placeholder slugs, null if >20% placeholder), and fill check against live CLOB depth. It discloses constraints like skipped_low_similarity and thin_legs. 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 somewhat lengthy but well-structured with paragraphs for each mode and additional details. Every sentence provides actionable information; there is no filler. It could be slightly more concise by grouping some details, but it remains clear and front-loaded with the main purpose.

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 (multiple modes, conditions, response structure) and the absence of an output schema, the description thoroughly explains the response format: opportunities[] with fields like gap_pp, suggested_trade, reasoning, and partition_check with gap_from_1, placeholders_filtered, etc. It also covers fill_check and references a sibling tool for custom sizing. The description is complete for an AI to use 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?

Schema description coverage is 100% (both parameters documented). The description adds significant meaning beyond the schema: explains acceptable event slug formats ('fed-decision-may-2026' and full URLs), provides seed question examples for topic, and clarifies behavioral differences between modes. This adds substantial value beyond the basic schema descriptions.

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 finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between three modes: no args (trending_scan), event (single event slug), and topic (cross-event). The verb 'Find arbitrage opportunities' plus the specific mechanisms and mode differentiation provide excellent purpose clarity.

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 gives explicit guidance on when to use each mode: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', 'topic (for cross-event scanning)'. It also warns when not to trade: 'realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it.' It mentions an alternative tool: 'For custom sizing use polymarket_fill_risk.' This is comprehensive.

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