score_alert_noise

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds value by explaining the algorithmic approach and the output format (score plus recommendations), but it does not disclose any behavioral nuances such as data sources or edge-case handling. This is adequate given the strong annotation coverage.

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 only two sentences, immediately stating the action and method, then describing the output. Every word contributes to understanding the tool's function and result. There is no filler or redundancy, making it an excellent example of concise, front-loaded writing.

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 description explains the return values (score 0-100, recommendations) and the core method, which is sufficient because all parameters are optional and fully documented in the schema. However, it does not clarify what data sources are analyzed or how 'noise' is defined, which could matter in a complex environment. With no output schema, the output description is helpful, but a touch more context would round it out.

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?

The input schema provides 100% description coverage for all three optional parameters, each with a clear explanation. The description itself adds no additional meaning about parameters, so it does not compensate beyond what the schema already offers. Baseline of 3 is appropriate because the schema carries the parameter semantics fully.

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 ('Score'), a specific resource ('alert noise level'), and the method ('Shannon entropy and flap detection'). It also defines the output scale (0-100) and includes recommendations, making the tool's purpose unmistakable. This level of specificity differentiates it from siblings like detect_alert_burst or get_alert_statistics.

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 provides no guidance on when to use this tool versus alternatives. It never mentions related tools like detect_alert_burst or get_alert_statistics, nor does it state any prerequisites or exclusion criteria. An agent must infer the appropriate context from the purpose alone.

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