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

Annotations already declare readOnlyHint=true, openWorldHint=true, and idempotentHint=true, so the agent knows it's a safe, non-mutating operation. The description adds valuable context about the comparison methodology (AWS benchmarks, CVE hardware risks) and the output (risk assessment with root causes), going beyond the annotations without contradicting them.

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 moderately concise but includes redundant elements like the keyword list and the persona 'As a CTO'. The first three sentences carry the core information, while the fourth is filler. It could be tightened without losing meaning.

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 covers the primary inputs (observed metrics), the output (risk assessment with root causes), and common use cases. It does not explain optional parameters (region, instanceType) or async behavior, but the presence of an output schema and annotations compensates for some of this. Overall, it provides enough context for an agent to decide when and how to invoke the tool.

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 only 20% (only 'async' has a description). The tool description mentions 'observed metrics (e.g., CPU utilization, request latency)', which hints at metricType and observedValue, but does not clarify units, expected value ranges, or the meaning of 'region' and 'instanceType'. It fails to compensate for the low schema 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 identifies anomalous cloud metrics (CPU, latency, memory) by comparing against AWS public benchmarks and CVE-linked hardware risks. This specific verb+resource+method distinguishes it from sibling tools like observability_log_pattern_miner (logs) and sre_slo_breach_predictor (SLO predictions).

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 explicitly lists ideal use cases: performance troubleshooting, security hardening, and capacity planning. It provides clear context for when to use the tool, but does not mention exclusions or alternative tools, so it falls short of a 5.

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