Grounded KPIs for this workspace: every metric is defined in terms of map fields (verified grounding) and carries the caveats inherited from the map — weak joins, known discrepancies. Numbers without caveats do not exist here. On a map larger than one call reads, the list is cut and says so: truncated is true and truncated_note explains.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even with annotations already declaring readOnlyHint=true and openWorldHint=false, the description adds valuable behavioral detail: metrics are verified against map fields, caveats are inherited, and results may be truncated with truncated and truncated_note set. It also states that unsupported numbers simply do not exist, which meaningfully shapes agent expectations.
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?
The description is compact and front-loaded with the core purpose, and each sentence adds information about grounding, caveats, or truncation. It is slightly dense and jargon-heavy, but it earns its length and contains no filler.
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?
For a zero-parameter, read-only tool with no output schema, the description covers the essential behavioral contract: what metrics are, what caveats to expect, and how truncation is signaled. It does not detail the shape of each returned metric, but the truncation semantics and grounding guarantees provide enough context for an agent to call it correctly.
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 has zero parameters and schema coverage is 100%, so there are no parameter semantics to clarify. Per the baseline for zero-parameter tools, the description does not need to compensate, and it does not introduce any misleading parameter-related claims.
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 identifies the tool as returning workspace-level KPI metrics that are grounded in map fields, and the phrase 'the list is cut' confirms it produces a list. It does not explicitly state a verb like 'list all metrics' and does not contrast itself with sibling get_metric, so it stops short of full sibling differentiation.
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 description implies the tool is for retrieving metrics, but it gives no explicit guidance on when to use list_metrics versus get_metric, list_findings, list_processes, or list_signals. No alternatives, exclusions, or selection conditions are mentioned, so an agent must infer the appropriate use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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