recommend_gpu_shape

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the safety profile is clear. The description adds no additional behavioral traits (e.g., rate limits, dependence on external data freshness). The description is consistent with annotations, so no contradiction.

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 concise sentence, front-loading the verb and resource. However, it is so brief that it omits important context, such as the fact that recommendations are based on the provided parameters. The sentence earns its place but could be slightly expanded for clarity.

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 tool has three parameters and no output schema, the description should provide more context about return values (e.g., does it return a single recommendation or a list?). It lacks guidance on how the recommendation is produced or what the output looks like, leaving the agent underinformed.

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 100% description coverage for all three parameters, so the schema already explains each parameter's meaning. The description does not add new semantics beyond what is in the schema. A baseline of 3 is appropriate.

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: 'Get GPU shape recommendation based on workload requirements and budget.' It uses a specific verb ('Get') and resource ('GPU shape recommendation'), distinguishing it from sibling tools like 'list_gpu_shapes' which simply lists available shapes, and 'get_gpu_shape_details' which details a specific shape.

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 no guidance on when to use this tool versus alternatives. It does not mention exclusions or context where other tools might be more appropriate, such as when a user needs a specific shape's details or wants to compare costs. Sibling tools like 'compare_gpu_shapes' or 'calculate_workload_cost' are not referenced.

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