List recent learned-baseline latency anomalies: observed vs baseline response time, the deviation z-score, how many consecutive hours it has been sustained, and the incident it auto-opened (if any). Call this when asked why a service is slow, about latency spikes, or for early degradation signals that have not yet tripped a hard up/down threshold.
| 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?
Annotations already establish that this is a read-only, non-open-world operation. Beyond that, the description discloses the useful behavioral detail that these are learned-baseline anomalies and that some have auto-opened incidents, which helps an agent interpret the results.
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 dense sentences front-load the purpose and then the usage conditions. Every phrase adds information about what is returned or when to call it, with 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?
With no input parameters and no output schema, the description carries the burden of explaining the result shape, and it does so by listing the key anomaly fields and the auto-opened incident relationship. It also explains when the tool is useful, making it complete 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?
The tool takes no parameters, so there are no parameter semantics to explain. The baseline for zero-parameter tools applies, and the description does not introduce any confusing parameter-like expectations.
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 uses a specific verb and resource ('List recent learned-baseline latency anomalies') and names the exact information returned (observed vs baseline, z-score, sustained hours, auto-opened incident). This clearly distinguishes it from the incident- and service-focused sibling tools.
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 clear triggering contexts: when asked why a service is slow, about latency spikes, or for early degradation signals. It does not explicitly name alternatives or when not to use it, but the usage context is strong enough for an agent to select it confidently.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
