detect_cost_anomalies

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

With no annotations, the description must carry the full burden of behavioral disclosure. It does not explicitly state that the tool is read-only, nor does it mention any side effects, output format, or how baseline spending is determined. The word 'detects' implies analysis, but the agent is left without confirmation of safety or what the tool returns.

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?

Two sentences, each earning its place: one states purpose/scope, the other gives usage guidance. No filler or 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?

For a simple detection tool with one optional parameter and no output schema, the description gives purpose and usage but does not indicate what the tool returns (e.g., a list of anomalies, a summary). This leaves an agent to infer the output format, which is a notable gap.

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% and the parameter min_spike_percentage is well-documented in the schema. The tool description adds no additional parameter semantics, so it meets the baseline for a fully documented schema.

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 it 'detects unusual cost spikes across AWS services compared to baseline spending' – a specific verb, resource, and scope. It also distinguishes from sibling tools by explicitly naming AWS, while siblings like detect_azure_cost_anomalies and detect_gcp_cost_anomalies target other providers.

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?

Provides explicit usage context: 'Use this when asked about billing surprises, unexpected charges, or cost spikes.' It does not mention when not to use or name alternatives, but the guidance clearly frames when this tool is appropriate.

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