get_aws_price

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

Without annotations, the description carries full burden. It discloses return fields and the 730-hour monthly calculation. It could mention that it performs a read operation (no side effects), but the provided detail is sufficient for a simple lookup.

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 a single, front-loaded sentence with no redundant words. Every part contributes to understanding the tool's purpose and behavior.

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 lookup tool with one parameter and no output schema, the description is highly complete. It covers purpose, input context, and output format. Minor gaps like error handling or rate limits are less critical given the simplicity.

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 coverage is 100% with a detailed description of allowed instance types. The description adds value by specifying the region (us-east-1) and OS (Linux), which are not in the schema, helping the agent understand the exact context of the parameter.

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 specifies the exact action ('Look up'), resource ('AWS EC2 instance type'), context ('on-demand Linux', 'us-east-1'), and return values ('vCPUs, memory, hourly USD, monthly USD'). It clearly distinguishes from sibling tools focusing on Azure, GCP, or comparisons.

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 implicitly states usage for AWS EC2 on-demand Linux in us-east-1, but lacks explicit guidance on when to use alternatives (e.g., Azure/GCP tools) or exclusions. An explicit mention of sibling tools would improve clarity.

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