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

Annotations provide readOnly/idempotent hints, and the description goes far beyond them by disclosing model-family logic, Kelly caps, net-of-slippage edge, a 24h-move warning, caching behavior, diagnostic fields, placeholder filters, and the reason Fed bets are excluded. It also explains why min_kelly does not apply to partition arbs, adding substantial behavioral context.

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 information-dense and front-loaded with purpose, but it is extremely long (roughly 350 words) and packs many implementation details that could be deferred to output documentation. While every sentence carries some information, the density makes it harder to scan quickly, so it is not optimally concise.

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, lack of output schema, and nine parameters, the description is exceptionally complete: it explains response segments, per-opportunity fields, knob interactions, diagnostics for empty segments, and caching. An agent would have enough context to select and invoke the 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 documents all 9 parameters, the description enriches them with operational meaning: min_liquidity/max_spread_pp 'drop opportunities where edge isn't realizable,' min_partition_leg_kelly applies to per-leg Kelly inside top_legs, and slippage_pp is explained in the context of zero fees but thin depth. This goes well beyond the schema's baseline.

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 states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price,' providing a specific verb, resource, and output scope. It also clarifies the intended use case ('what should I bet on today') and differentiates from sibling tools by describing proprietary model families and segments.

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?

Clear use context is given: it is built for discovering betting opportunities without paging hundreds of markets, and it explicitly notes when to be cautious (Fed candidates are excluded from ranking due to unreliable signal). However, it does not name alternative tools for cases like pure arbitrage or fill-risk analysis, so exclusion/alternative guidance is incomplete.

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