Tail stdout/stderr from a running Isaac Lab training container to check progress or errors; pass the run name and optional tail count.
Tail a running training container's stdout/stderr.
| Name | Required | Description | Default |
|---|---|---|---|
| tail | No | ||
| run_name | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Tail' and 'stdout/stderr' disclose that this is a read of log output and hint at streaming/last-N-lines behavior, but it never states whether the call blocks or streams, whether it is read-only, or how it behaves for a stopped container.
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 tight sentence with the key constraint ('running') front-loaded. No filler, no redundancy.
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 described. The description covers the core action but leaves gaps for a log-retrieval tool: streaming vs snapshot behavior, handling of a non-running container, and the meaning of the tail count are all unstated.
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%, so the description must compensate. The verb 'tail' loosely maps to the integer 'tail' parameter (default 200, implying number of lines), and 'training container' implies run_name selects the target, but neither parameter is explicitly explained in terms of format or semantics.
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 (tail) and resource (a running training container's stdout/stderr), which is far more informative than the bare name train_logs. It implicitly distinguishes itself from siblings like train_metrics and train_status by scoping to raw container output, 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?
The phrase 'a running training container' implies the tool is only applicable while training is active, which is useful context. However, there is no explicit guidance on when to use this versus train_metrics or train_status, and no statement of what happens if the run has finished.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.