Retrieve a Dynatrace metric's full descriptor—unit, dimensions, aggregations, and transformations—to understand how to query and interpret its data.
Full descriptor of one metric: unit, dimensions, aggregations, transformations.
| Name | Required | Description | Default |
|---|---|---|---|
| metric_key | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
readOnlyHint=true already tells the agent this is a safe read. The description adds value by disclosing what the response contains (unit, dimensions, aggregations, transformations), which is useful since there is no output schema. It does not say what happens for an unknown metric_key, nor whether the key must come from list_metrics.
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?
A single front-loaded sentence with no waste; the resource comes first and the returned facets follow. It is terse to the point of under-specification, but nothing is redundant.
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 output schema, listing the descriptor's facets does real work in describing the return value. Still missing are the metric_key format/source and error behavior, which matter for a one-parameter lookup tool.
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?
Schema description coverage is 0% and metric_key has no documentation anywhere. The phrase 'one metric' weakly implies the parameter is a single metric identifier, but the expected format (dotted name, ID, enum source) is left entirely undefined.
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 a specific resource and scope: the full descriptor of a single metric, with the returned facets (unit, dimensions, aggregations, transformations) enumerated. It is clearly a metadata-lookup, distinct in spirit from list_metrics/query_metrics, but it never names or contrasts those siblings 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?
No when-to-use guidance, no prerequisites, and no mention of the obvious alternatives (list_metrics to discover keys, query_metrics to fetch values). The agent must infer that this is a pre-query metadata inspection step.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.