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

Annotations already cover read-only/idempotent/no-destructive, and the description adds the non-structured requirement 'Requires api:read' plus an enumeration of the graded outcome states (played_out, invalidated, expired, active). It still omits scale/volume and pagination characteristics, but since no output schema exists the field-level disclosure is valuable.

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 purpose is front-loaded and every clause maps to a real output section, but it is delivered as one very long, jargon-dense sentence with nested parentheticals. 'Case auto-resolution counts' is unexplained, forcing the agent to guess at what a 'Case' is.

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?

With no output schema, the description does real work by enumerating the four report sections and the classification taxonomy (momentum continuation detectors like options_unusual vs. mean-reversion fades like funding_extreme/basis_dislocation/liquidation_cluster). Gaps remain around output shape and undefined 'Cases', but an agent knows what it will get and that it needs api:read.

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 coverage is 100% with all three filters (category, detector, window_hours) described including examples and the 1-720 range, so the schema does the heavy lifting. The description adds no filter semantics or interaction notes (e.g., how the window applies to already-closed opportunities), keeping this at the 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 names a specific resource (an empirical scoreboard over Today's Market opportunities and tracked Gambits) and enumerates its four output areas: barrier grading, calibrated detector edge, lead-lag latency, and Case resolution counts. It is clearly a read-only analytics aggregate rather than a per-opportunity lookup like get_opportunities or get_opportunity_detail, though it never names those siblings explicitly.

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?

Usage is only implied: the 'closed-loop empirical' framing signals this is for evaluating historical detector performance rather than fetching a live opportunity list. There is no explicit when-to-use, when-not-to-use, or pointer to the alternative tools (evaluate_opportunity, get_opportunities) an agent would otherwise weigh.

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