colab_poll

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds value by detailing the response statuses (starting/running/completed/failed/cancelled), cleanup after 5 minutes, and common issues, providing rich behavioral context beyond the annotations.

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 well-structured with sections for usage, response interpretation, common issues, and args. It is front-loaded with the purpose and avoids unnecessary words, though it is slightly lengthy.

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 existence of an output schema, the description adequately covers all necessary context: usage, response interpretation, common issues, and parameter semantics. It is complete for a polling tool.

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%, but the description compensates by explaining job_id as 'The job identifier returned by colab_execute', adding meaning to the only 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 clearly states 'Poll a background job for its current status and results' and distinguishes itself by explicitly directing usage after colab_execute(..., background=True), differentiating from sibling tools like colab_jobs and colab_cancel.

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?

It explicitly says when to use ('after launching a background execution with colab_execute(..., background=True)') and provides context for when not to use or alternatives (e.g., 'use colab_jobs to list all tracked jobs' for unknown job_id).

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