train_cloud

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

With no annotations, the description carries the full transparency burden. It compensates well by disclosing virtual-key credential boundaries, no plaintext credentials, SSH-based stdout JSON returns, a sensitive-file sorting gate into the audit chain, disconnection loss-stop behavior, and cost budgeting. It does not, however, describe side effects of removal or error/failure behavior.

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 information-dense and every clause contributes content, but the single long sentence with slash-separated actions and embedded security clauses is heavy and harder to parse quickly. Better structuring would improve scannability without losing detail.

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's multi-action complexity, absence of annotations, and lack of an output schema, the description is not fully complete. It covers remote training output and several safety mechanisms, but it does not specify return shapes for add/list/status/remove, failure modes, or selection criteria relative to sibling training tools.

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%, so the baseline is 3. The description restates endpoint and credential-reference semantics and adds the SSH/stdout-JSON behavior, but it does not materially extend the schema's parameter documentation or clarify conditionally required fields beyond what the schema already states.

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 identifies the resource as a cloud VM execution surface and enumerates concrete operations: register, list, check status, remove, and remote spawn training via SSH. This is enough to separate it from most sibling tools by context, though it does not explicitly name or contrast any sibling and blends VM management with training execution.

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?

No explicit guidance is given about when to use this tool versus sibling tools such as train_submit, train_status, or train_list. The cloud VM context is implied, but there are no stated conditions, exclusions, or alternatives, leaving an agent to infer selection criteria.

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