pmus_scan_extreme_markets

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it uses 'event snapshot quotes' (not live order book), computes fee-adjusted annualized yield, and explicitly warns to confirm with pmus_analyze_spread before acting. This goes beyond the annotations and helps the agent understand the data source and the need for verification.

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 three sentences, each earning its place: the first defines the scan scope and criteria, the second details the computed metrics, and the third gives a critical usage warning. It is front-loaded with the core purpose and avoids fluff. No redundant repetition of schema or annotation information.

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?

The tool has 9 parameters, an output schema, and rich annotations. The description covers the purpose, the key thresholds, the computation logic, and the confirmation step. It doesn't explain the output schema in detail, but the output schema exists and the description doesn't need to. The only minor gap is that it doesn't explicitly state what the return format is, but the output schema covers that. Overall, it is complete enough for an agent 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?

Schema description coverage is 67%, so the schema documents most parameters. The description adds meaning by explaining the core thresholds (high_threshold, low_threshold) in terms of 'heavy favorites' and 'long shots', and by clarifying that the scan is over 'event pages of 100' (which maps to the pages parameter). It doesn't document every parameter, but the key ones are given semantic context beyond the schema.

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 a specific verb ('Scan'), a specific resource ('active markets'), and a precise selection criterion ('extreme implied probabilities' with heavy favorites and long shots defined by thresholds). It also names the sibling tool it is not (pmus_analyze_spread) and explains the computed outputs (fee-adjusted yield to settlement, spread width, resting vs crossing). This clearly distinguishes it from the other pmus_* tools.

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 says to use pmus_analyze_spread to confirm before acting, which is a clear when-to-use/alternative guidance. It also implies this is a scanning/screening tool for finding opportunities, not for execution or detailed analysis. The context of 'active markets' and the thresholds make the intended use case clear.

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