aiworkhub_dashboard_task_live_output

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

Annotations already declare readOnlyHint=true, and the description corroborates rather than contradicts by noting it reads only that task's log files without mutating state and validates task_id format. It falls short of disclosing end-of-stream behaviour, truncation, or rate/paging limits, so it is strong but not exhaustive.

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 dense sentences with the READ-ONLY guarantee front-loaded and the sibling routing in the second. No filler; every clause 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?

With an output schema present, the description needn't explain return values, and annotations cover the safety profile. Minor gaps remain around how a caller signals/paginates to completion, but nothing required to call it correctly is missing.

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 100% and the schema itself documents cursor clamping and default. The description still adds value by framing cursor as a byte offset for incremental paging and clarifying that task_id resolves to the active or latest worker run, which is not in the schema.

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 (read), a specific resource (a bounded page of live stdout/stderr for one task's active or latest worker run), and scope (bounded page, single task). It also explicitly distinguishes itself from aiworkhub_dashboard_task_detail and aiworkhub_dashboard_snapshot by function.

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?

Gives explicit routing: use this for streaming/paging incremental worker output from a byte cursor, use task_detail for the task contract and attempt history, use snapshot for queue-wide status. An agent can select the right tool without inference.

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