alarm_cascade

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds valuable behavioral context beyond that: it reveals the first-out heuristic is 'transparent' and 'cited by timestamp — NOT causal,' notes the tool is 'bounded,' and explains it collects live via the 'OPC-UA active-condition scan.' This enriches the agent's understanding beyond the structured 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 well-structured with sections for Args, Returns, and Example, and the opening line is an immediate purpose statement. It is longer than some descriptions, but every sentence adds necessary detail for correct usage. Slightly verbose but not wasteful, earning a 4.

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 complexity (5 parameters, no schema descriptions, no output schema), the description is comprehensively complete. It covers the return dict structure with all nested fields, explains the cascade logic, clarifies the heuristic nature, and provides a concrete example. This gives the agent everything needed to 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 0%, so the description carries full responsibility for parameter meanings. It does this excellently by explaining each parameter: 'endpoint', 'duration_s' (with range 1..300), 'window_s' (quiet gap), 'min_cascade', and 'events' (with format {source, timestamp, state?}). It also provides an example call, making usage clear.

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's function: 'Collapse an alarm flood into cascades + each cascade's first-out root.' This uses a specific verb ('collapse') and resource ('alarm flood'), and it precisely answers the question 'which alarm to look at first.' It also distinguishes itself from sibling tools like 'downtime_root_cause' by explicitly stating it is a heuristic, not causal.

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 usage context: 'when 100+ alarms hit in minutes' and explains the tool answers 'which alarm to look at first.' It clearly differentiates from alternatives by stating 'NOT causal (use downtime_root_cause for causality)' and clarifies when to use live collection vs. pure analysis with 'Pass 'events' for pure analysis, or an endpoint to collect live.'

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