compare_to_baseline

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description does not contradict these. It adds useful output semantics (deviation percentages and statuses) but does not disclose potential failure behavior, such as what happens if the named baseline does not exist.

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 two sentences, front-loaded with the action and followed by the output contract. Every word earns its place, with no repetition of schema details or filler.

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?

Since there is no output schema, the description correctly provides the essential return information (deviation percentage and status values per datapoint). It omits details like default hours_back and baseline fallback behavior, but these are already covered by the schema, so the combination is sufficient.

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 100%, so all five parameters are already documented in the input schema. The description only summarizes the overall operation and does not add parameter-level meaning beyond what the schema provides.

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 verb ('Compare'), a resource ('current metrics against a stored baseline'), and the output contract ('deviation percentage and status ... per datapoint'). This clearly distinguishes it from related siblings like save_baseline or get_metric_anomalies.

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 implied: use this when current metrics need to be checked against a saved baseline. However, it does not explicitly state when not to use it or name alternatives, so an agent is left to infer routing decisions from the tool name and context.

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