aiworkhub_supervisor_loop_status

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

Annotations already declare readOnlyHint=true, but the description adds substantial behavioral context: frozen read-only contract, no write-gate toggle, no mutation parameters, never calls taskctl done/review/start/auto-pickup/add-card, never mutates queue/audit state, never launches agents, and writes remain default-off. This is rich disclosure beyond what annotations provide.

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?

Front-loads the key purpose, but includes internal implementation details (e.g., the B08 contract file name, core.show_task, taskctl command names) that may not be relevant to an agent selecting the tool. The repeated read-only warnings are useful but make it longer than necessary.

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?

The behavioral safety profile is complete and an output schema exists, so return values need not be explained. However, with 6 parameters and 0% schema coverage, the description fails to provide parameter semantics or usage guidance, which are needed for correct invocation.

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% for all 6 parameters, and the description does not explain the meaning or expected format of topic, runner, previous_snapshot, supervisor_request_id, or reported_validation_verdict. Only task_id is mentioned in passing, leaving most parameters undocumented.

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: 'derive supervisor_state + error taxonomy for a task_id' and describes the 7-step derivation. It does not explicitly distinguish itself from the similarly named sibling aiworkhub_manager_loop_status, leaving an agent to infer the difference from context.

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?

Explains what the tool derives but never says when to use it versus alternatives such as aiworkhub_manager_loop_status or aiworkhub_task_health. There is no 'use this when' guidance or exclusion criteria.

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