colab_process_output

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

No annotations are provided, so the description carries the disclosure burden. It adds useful behavioral context: output is retained in a local spool, pagination via next_offset is required, and the spool survives runtime loss. It does not mention side effects (though 'read' implies non-destructive) or error cases, but the key durability and pagination behaviors are disclosed.

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?

Two short, front-loaded sentences. The first gives the core action; the second provides essential stateful guidance. No wasted words, efficient and clear.

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 no output schema, the description should explain return values. It hints at next_offset being returned but does not describe the content format, stream handling nuances, or how errors manifest. For a paginated read with five parameters, more detail would improve completeness, though the schema covers parameter meanings.

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 100%, so the baseline is 3. The description reinforces the offset behavior with 'Keep next_offset' but does not add new details beyond the schema, which already explains the offset semantics and process_id requirement.

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 the tool reads retained output, with a specific verb (read) and resource (retained output). It distinguishes the operation from process management tools like colab_process_status or colab_process_export, though it does not explicitly name alternatives. The purpose is unambiguous and aligned with the tool name.

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 use case is implied: reading retained process output. The note about runtime loss provides a specific scenario where the tool remains useful, giving some contextual guidance. However, it does not explicitly contrast with alternatives like colab_process_export or when not to use this tool, so guidance is limited.

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