get_system_health

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

With no annotations, the description carries the full burden and does disclose the return shape, including the health indicator semantics (🟢/🟡/🔴) and that percentages are included. It omits cost/latency notes and aggregation window details, but for a zero-parameter read-only aggregate this is substantially informative.

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?

Three sentences, front-loaded with the headline behavior, then the return contents, then the comparative advantage. No filler or restatement of the name.

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?

No output schema exists, but the description compensates by enumerating the metric categories and indicator format an agent will receive. Combined with zero parameters, an agent has everything needed to decide to call it and interpret the result.

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 zero parameters, so there is no parameter semantics to explain; the baseline for a no-param tool applies. The description correctly implies no filtering or scoping input is accepted.

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?

Opens with a specific verb+resource ("Get all OmniFocus health metrics in a single call") and enumerates exactly what is returned: inbox counts, projects by status, tasks by status, tags, flagged/untagged tasks. It also differentiates itself from the many list_/filter_ siblings by framing itself as the aggregate dashboard call.

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 a clear context for use ("for dashboards") and names the alternative it replaces ("multiple filter_tasks calls"). It stops short of explicit when-not guidance — e.g. it doesn't say to prefer the specific list_* tools when only one metric is needed — but the routing signal is strong.

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