wait_for_first_metrics

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

No annotations are provided, so the description carries the behavioral disclosure burden. It clearly discloses that the tool blocks until data appears, provides a typical duration estimate of 1-2 minutes, and explains timeout and polling behavior through the args. It does not detail timeout failure behavior, but the core blocking nature is transparent.

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 front-loaded with the main purpose, then gives a usage hint and timing expectation, followed by a compact Args list. Every sentence adds value and there is no redundant filler.

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 polling tool with an output schema available, the description covers the key operational context: what it waits for, when to call it, and how long it typically takes. It could have explicitly contrasted with siblings like wait_for_completion, but this is a minor gap given the clear first-metric focus.

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 does so fully by defining all three parameters: run_id ('The run to watch'), timeout ('Max seconds to wait'), and poll_interval ('Seconds between checks'). This adds clear meaning beyond the bare schema.

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 uses a specific verb ('Block') and resource ('training run produces its first TensorBoard metric data point'), making the tool's function unambiguous. It also distinguishes itself from siblings like wait_for_completion by focusing on the first metric data point rather than overall completion.

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 clearly states when to use the tool: 'Use this right after starting training to know when data starts flowing.' It does not explicitly mention alternatives or when not to use it, but the context is clear enough to guide an agent.

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