colab_runtime_info

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

With no annotations, the description carries the full behavioral burden. 'Report' implies a non-destructive read and '(if any)' hints at a conditionally-present GPU field, which is useful. However, it does not disclose session prerequisites, auth requirements, or whether it spins up a runtime.

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 well-formed sentence with the resource scope front-loaded and zero wasted words. The parenthetical GPU qualifier adds precision without padding.

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-value explanation is rightly omitted, and for a read-only info tool the description is nearly sufficient. The unexplained 'session' parameter and absent guidance on session prerequisites leave a real gap.

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% and the single 'session' parameter has no documentation in either the schema or the description. Since coverage is below 50%, the description should compensate but does not mention the parameter at all.

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 ('Report') and enumerates exactly what is reported: Python version, CPUs, RAM, free disk, and GPU. This lets an agent distinguish it from generic siblings like colab_status or colab_usage. It stops short of explicitly naming which sibling to use instead, so it does not reach a 5.

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?

There is no when-to-use guidance, no prerequisites (e.g., whether an active session is required), and no mention of alternatives such as colab_status or colab_usage. The agent must infer context entirely on its own.

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