signallord_get_cycle_top_probability

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint, setting a baseline. The description adds behavioral detail by explaining the aggregation of multiple signals and output fields (current.signals, delta_30d), which is informative and non-contradictory.

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 concise with three sentences, front-loading the key purpose and then detailing output fields. Every sentence adds value without redundancy.

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 an output schema present, the description does not need exhaustive return value details, but it highlights key output fields (signals, delta_30d). The tool has only two optional parameters, so the description sufficiently orients the agent for invocation.

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 50% with only 'days' and 'response_format' described. The description does not add parameter-specific meaning beyond the schema, but it does explain the output fields which indirectly helps parameter understanding. Baseline score of 3 is appropriate given the 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 clearly states the tool computes a composite cycle top probability score (0-100) by aggregating multiple topping signals like Pi Cycle proximity and funding euphoria. It distinguishes itself from siblings like get_cycle_bottom_probability by specifying the direction (top) and the output fields that list triggering signals.

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 implies use when assessing cycle top likelihood but does not explicitly guide when to use this tool over alternatives like get_composite_score or get_cycle_bottom_probability. No exclusion criteria or context for appropriate use is provided.

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