get_ai_billing_blind_spots

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

With no annotations provided, the description must convey all behavioral traits. It explains that the tool flags spend invisible to AWS anomaly detection and that 'nable watches them directly,' implying a read-only query. However, it does not explicitly state whether it is read-only, destructive, or any authorization requirements, leaving ambiguity about safety.

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 concise: a single paragraph defining the purpose, a one-line argument spec, and two relevant examples. It is well-structured with key information front-loaded, and every sentence adds value.

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 moderate complexity of the tool and no output schema, the description could be more complete by specifying the output format (e.g., list of flagged items, totals, or grouping). The examples imply what users might see but do not formally describe the return structure.

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 description provides a clear explanation of the only parameter, 'days', as a lookback window with a default of 30, which adds meaning beyond the schema's raw definition. Since schema coverage is 0%, the description compensates effectively.

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 purpose: to flag AWS AI/Marketplace spend that bypasses AWS Cost Anomaly Detection. It specifies the services involved (Bedrock, Marketplace AI/SaaS, SageMaker) and distinguishes itself from related tools like get_bedrock_costs or get_marketplace_costs by focusing on spend invisible to anomaly detection.

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 clear context on when to use the tool: to find unmonitored AI/Marketplace spend. The examples (e.g., 'What AI spend is AWS not watching for anomalies?') illustrate typical user intents. However, it does not explicitly state when not to use it or mention alternative tools, leaving some gap in comprehensive guidance.

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