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Describe Alerting

describe_alerting
Read-onlyIdempotent

The static measure catalog for authoring an alert rule: per source (LOGS, SPANS, METRICS), the measure functions available, each with its unit and defaultMode (THRESHOLD or ANOMALY — the mode a new rule on this measure should default to). READ: available to any authenticated user. This is a static catalog: it reads no telemetry and returns the same answer for every caller.

Call query's describe_schema first for the tenant's services, groupable fields, and metric names (pass source=metrics for the metric list) — this tool no longer returns any of that. Use this tool only to pick a measure once you know the source and, for METRICS, the metric's kind.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / $schema
      Added value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
  2. Added
  3. Removed
  4. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive/openWorld=false, and the description adds auth scope ('READ: available to any authenticated user') plus the important determinism trait ('reads no telemetry and returns the same answer for every caller'). That is real behavioral context beyond the structured hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core definition and kept to a few dense sentences, each carrying information. The parenthetical nesting (per source ... each with ...) makes the opening slightly heavy, but no sentence is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description must carry the return-shape burden, and it does describe the payload (functions per source, unit, defaultMode) reasonably well. It stops short of describing the exact nesting/format of the response, which is the only real gap for a zero-param catalog tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Zero parameters, so the baseline is 4; there is nothing for the description to clarify. It does name the source enum values (LOGS, SPANS, METRICS) and the THRESHOLD/ANOMALY modes, but those describe the return payload rather than inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (static measure catalog for authoring alert rules) and its exact contents (measure functions per source with unit and defaultMode). It explicitly differentiates itself from describe_schema, which the agent could otherwise confuse it with.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit sequencing ('Call query's describe_schema first ...'), an explicit scope limiter ('Use this tool only to pick a measure once you know the source and, for METRICS, the metric's kind'), and notes what this tool no longer returns. Nothing is left to inference.

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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