Explains the server's health data source, API, read-only boundaries, and availability limits so you can interpret metric results correctly. Call before analyzing metrics.
Explain exactly where this server gets health data, which API it uses, its read-only boundaries, and known availability limitations. Call this before interpreting metric results.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.2.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent/non-destructive/openWorld. The description adds value beyond them by signalling that read-only boundaries and availability limitations are surfaced, which matters for interpreting results. It doesn't cover return format or auth, but the key behavioral context is present.
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 tight sentences with the purpose front-loaded and the usage instruction second. No filler; every clause earns its place.
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-param informational tool with no output schema, the description adequately explains what information is returned and why it matters. The exact shape of the response is left unspecified, which is the only minor gap.
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?
Zero parameters, so baseline is 4. There is nothing for the description to disambiguate at the parameter level, and none of the field semantics undermine correctness.
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 precisely what the tool returns: data source, API used, read-only boundaries, and availability limitations. It carves out a distinct informational role relative to the metric-fetching siblings, though it doesn't name them explicitly.
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?
Explicitly says 'Call this before interpreting metric results,' giving a clear when-to-use trigger. It doesn't enumerate when-not-to-use or name a specific sibling, but the precedence rule is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.