colab_run

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

With no annotations, the description carries full behavioral burden, and it does disclose the important lifecycle (allocate fresh VM, run, release, or keep with keep=true) and the working-directory constraint. It omits gpu/high_mem effects, timeout behavior, and what happens to the VM on script failure, so the disclosure is partial rather than complete.

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 dense sentence that front-loads the execution model and never wastes a clause; every phrase (fresh VM, local script, working directory, keep=true) carries information.

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 no explanation, and the lifecycle is covered. But for a resource-allocating mutation with zero annotation coverage and 6 undocumented parameters, the agent still lacks guidance on gpu selection, timeout semantics, and failure behavior.

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% across 6 parameters, so the description must compensate. It adds real meaning for script ('local', 'inside the working directory'), args (passed to the script), and keep (retains the VM), but gpu, timeout, and high_mem are left entirely undocumented in both schema and description.

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?

States a specific verb and resource (run a local script) and qualifies the execution model as a 'one-shot job' that allocates a fresh VM and releases it. This distinguishes it from persistent-session tools like colab_exec or the colab_job_* family, though it never names a sibling explicitly.

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?

'One-shot job' implicitly signals when this is appropriate versus session- or job-based alternatives, and 'keep=true' hints at the persistent path. However, there is no explicit when-to-use/when-not statement or named alternative (e.g. colab_exec vs colab_job_start), so the agent must infer routing.

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