ncloud_datafence_get_tensorflow_server

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

readOnlyHint=true already tells the agent this is a non-mutating read. The description adds only the CPU/GPU distinction, which is thin behavioral context and reveals nothing about auth requirements, return shape, or error conditions. With annotations covering the safety profile, a 3 is appropriate but not higher.

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?

A single short sentence, front-loaded with the operation and resource, with zero filler. Nothing could be trimmed without losing the CPU/GPU qualifier.

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 read-only getter with full schema coverage, readOnlyHint annotation, and no output schema requirement, the definition is essentially complete. The only missing element is routing guidance toward sibling getters, which is minor for this tool class.

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 each parameter description even routes the agent to the relevant sibling list/get tool for the ID. The description itself adds nothing about the three IDs, so the baseline 3 applies since the schema does all the work.

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 names a specific verb and resource (get a Box Tensorflow server's details) and the parenthetical '(CPU or GPU)' narrows the target. It implicitly distinguishes from sibling getters like ncloud_datafence_get_linux_server and ncloud_datafence_get_hadoop_cluster by naming the Tensorflow server type, though it never explicitly contrasts them.

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?

There is no statement of when to call this versus alternatives such as ncloud_datafence_get_box_summary or ncloud_datafence_get_connect_server, and no prerequisites (e.g., that a box must already exist). The only contextual help is indirect, via schema param descriptions pointing to list_boxes/get_datafence/list_box_infra for ID lookup.

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