get_bedrock_costs

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It describes the tool as a query that shows cost breakdowns, but does not explicitly state it is read-only, mention permissions, rate limits, or behavior with no data. It is adequate but not comprehensive.

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 with four sentences, including an arguments section. It is front-loaded with the main purpose and each sentence is valuable. No redundancy or unnecessary text.

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 tool has an output schema, so the description doesn't need to detail return values. It adequately covers input parameters and high-level output. For a relatively simple tool with two parameters, it is complete enough, though additional context about data source or time zone could be helpful.

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 has 0% description coverage, so the description compensates by explaining both parameters: days (number of days, default 30) and account (reserved for future use). This adds clear meaning beyond the schema's type and default values.

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 breaks down Amazon Bedrock costs by model and token type, listing specific details like spend per model, input/output split, cost per 1k tokens, and trend vs prior period. This clearly distinguishes it from sibling tools like get_llm_costs or recommend_bedrock_model_routing.

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 implies usage for Amazon Bedrock cost analysis, and the arguments are explained. However, it does not explicitly specify when to use this tool versus alternatives, or when not to use it. The context is clear enough for an agent, but lacks explicit exclusions.

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