aiworkhub_dispatcher_health

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the leading 'READ-ONLY' restates structured data rather than adding it. The description does add real behavioral content by listing the diagnostic signals surfaced (dispatcher state, pending callback count, last error), which tells an agent what evidence a failure yields. It stops short of saying what a 'stopped' result implies for remediation or whether the probe has side effects on the callback lane.

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 sentences with no filler: the safety marker and the reported fields come first, then the usage routing. Every clause carries information an agent would otherwise have to guess.

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 is not obliged to explain return shape, and it appropriately covers when to call and what signals to expect. The one gap is that it names alternatives for adjacent symptoms but not the obvious remediation sibling aiworkhub_dispatcher_ensure_started for a stopped dispatcher.

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?

The tool takes no parameters, so there is nothing for the description to disambiguate; the baseline for a zero-parameter tool applies. No parameter text is needed and none is wasted.

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 names the resource (the dispatcher) and enumerates exactly what the check reports: running/stopped state, selected coordinator target, repository identity, pending callback count, and last delivery/error. That is specific enough for an agent to distinguish it from aiworkhub_source_graph_health and aiworkhub_doctor_diagnose without opening any schema.

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 gives an explicit trigger ('use this when callbacks stop arriving') and routes two adjacent cases elsewhere by name: source_graph_health for stale Source Graph answers and the doctor check for a cross-subsystem verdict. When-to-use and alternatives are both stated.

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