train_status

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

No annotations are provided, so the description carries the full disclosure burden. It does add one behavioral trait — only the *latest* scalar values are returned, implying history lives elsewhere — but says nothing about permissions, whether the tool blocks on a running container, or how a missing run is handled.

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 front-loaded sentence with no filler; the most important information (what comes back, for how many runs) leads the sentence.

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?

An output schema exists, so return values need not be spelled out. However, with five overlapping train_* siblings and no annotations, the description should clarify how this differs from train_metrics, train_checkpoints, and train_logs — that routing information is missing.

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 0% and the single parameter run_name has no schema description. The phrase 'for one run' hints that run_name selects a single run, which is weak but non-zero compensation for the undocumented 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 names a specific resource and its payload (container state, checkpoints, latest TensorBoard scalars) scoped to one run, so an agent knows it is a read/status tool. It lacks an explicit verb and does not distinguish itself from the overlapping siblings train_checkpoints, train_metrics, and train_logs, which cover much of the same ground.

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 when-to-use guidance, no prerequisites, and no mention of any alternative. Given five sibling train_* tools with overlapping semantics, the absence of routing guidance is a real gap.

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