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datadog-mcp-server

create-rum-metric

Create a count or distribution metric from RUM events, with filters and group-by tags.

Instructions

Create a metric based on RUM data (count or distribution, with filters and group-by)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe name of the rum-based metric. Example: rum.page_view_count
eventTypeYesThe RUM event type to use. Example: view
aggregationTypeYesAggregation type. 'count' counts events, 'distribution' creates a distribution metric
pathNoPath to the metric value. Required for distribution metrics. Example: @view.loading_time
includePercentilesNoWhether to include percentile aggregations. Only for distribution metrics
filterQueryNoRUM search query to filter events. Example: @service:my-app @view.url_path:/checkout
groupByNoFields to group the metric by
uniquenessWhenNoWhen to count uniqueness. Only for session/view event types. 'match' = when event matches, 'end' = when session/view ends
Behavior3/5

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

Annotations indicate readOnlyHint=false (mutates) and openWorldHint=true. The description adds operational context (filters, group-by) but does not detail side effects, permissions, or lifecycle. Consistent with annotations, no contradiction.

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

Conciseness5/5

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

Single concise sentence that is front-loaded with key action and resource. No wasted words, appropriate length.

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

Completeness2/5

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

No output schema, and the description does not explain what the created metric does, where it appears, or how to use it later. Lacks completeness for a creation tool, especially with 8 parameters.

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

Parameters3/5

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

Schema coverage is 100%, so the description adds little beyond summarizing parameter options (e.g., 'count or distribution'). It does not provide additional semantics not already in the schema. Baseline 3 applies.

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?

The description clearly states the verb 'create', the resource 'metric based on RUM data', and specifies the capabilities (count or distribution, with filters and group-by). This distinguishes it from siblings like 'create-logs-metric'.

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

Usage Guidelines3/5

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

The description implies usage for creating RUM metrics but lacks explicit guidance on when to use this tool versus other metric creation tools or when to choose count vs distribution. No when-not-to-use or alternative suggestions.

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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