colab_run_training

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

With no annotations, the description carries the full burden and does add useful behavioral detail: it is asynchronous, returns a job_id immediately, and requires polling for status/logs. However, it omits other relevant behaviors such as whether an existing session is required, what happens on timeout, or how to cancel the job—gaps that an agent might need to know for reliable use.

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 two sentences with no filler; it front-loads the core purpose, then immediately tells the agent how to monitor the job. Every phrase adds value, and it is concise enough to be parsed quickly.

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 lack of output schema and annotations, the description covers the essential flow (start background job, get job_id, poll) but leaves important contextual details unspecified, such as session_id optionality, timeout behavior, and cancellation. For a training-run starter that is moderately complex, the description is adequate but not fully complete.

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?

Only the code parameter has a schema description ('Full training script to execute'), and the overall schema coverage is 33%. The description partially compensates by clarifying that the code is Python and can use any framework, and its phrase 'to a runtime session' hints at the session_id parameter. However, session_id and timeout_seconds are not explained, leaving those parameter semantics vague.

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 a specific action ('Start a training run') and resource ('background job'), and further specifies that it submits Python training code to a runtime session and returns a job_id immediately. This differentiates it from sibling execution tools like colab_execute_code by focusing on training runs and asynchronous background processing, so an agent can immediately tell it apart.

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 context: use this tool to launch a training run as a background job and then poll with colab_get_job / colab_get_logs. It does not explicitly name alternative tools for synchronous or non-training code execution, nor state when not to use it, but the background-job framing and polling instructions make the intended usage scenario clear.

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