fleet_status

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

Since no annotations are provided, the description carries full burden for behavioral disclosure. It transparently states the probing method (SSH), the expected latency (1-5 s), the failure handling (hosts returning ok=false instead of raising), and the definition of a busy GPU (process holds it or >5% memory taken). It also explains optional behaviors (processes=true) and the returned structure. This is comprehensive behavioral transparency.

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 well-structured and concise, with the primary purpose and scope in the first sentence, then a detailed but organized breakdown of the return format. Every sentence adds value: timing, error behavior, busy definition, and optional process details. There is no fluff or redundancy, and the structure supports quick comprehension.

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 only two optional parameters, no annotations, and no output schema, the description is exceptionally complete. It details the summary line, per-host fields, per-GPU fields, the busy threshold, and failure handling. An agent has all necessary information to invoke the tool correctly and interpret the response, even without schema definitions for the output.

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 0%, so the description must compensate. It effectively explains the 'hosts' parameter via 'every configured host (or just the named ones)' and the 'processes' parameter via 'with processes=true'. While it doesn't map parameter names explicitly, the meaning is clear from context. The description adds significant semantic value beyond the bare schema, though it could be slightly more direct in naming the parameters.

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: 'Live GPU status of every configured host (or just the named ones)'. It specifies a concrete verb (get status) and resource (GPU status of hosts), and it distinguishes itself from siblings like list_hosts (which lists hosts) and find_gpus (which finds GPUs) by focusing on real-time GPU metrics. The phrasing is specific and unambiguous.

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 clear operational context (e.g., 'probed now over ssh', 'takes 1-5 s') and scoping ('every configured host or just the named ones'), which implies when to use it. However, it does not explicitly mention alternatives or conditions for choosing this tool over siblings like list_hosts or find_gpus. There is no direct exclusion or routing guidance, but the context makes the usage reasonably clear.

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