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datadog-mcp-server

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create-rum-metric

Create a count or distribution metric from RUM events, filtered and grouped by custom fields, to track user experience.

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
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only states 'create' but does not disclose potential side effects like overwriting existing metrics, permission requirements, or id uniqueness constraints.

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?

The description is a single, front-loaded sentence that efficiently communicates the core purpose. It is appropriately concise given the schema's completeness.

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?

With 8 parameters and no output schema, the description lacks important context such as return value, idempotency, or validation rules. The brief summary does not sufficiently prepare an agent for actual usage.

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?

The input schema has 100% description coverage, so the schema itself explains each parameter. The description adds no additional parameter-level meaning beyond summarizing the types of metrics.

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 tool creates a RUM metric, specifying it can be count or distribution with filters and group-by. This distinguishes it from sibling tools like create-logs-metric or create-rum-application.

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 does not provide explicit guidance on when to use this tool versus alternatives such as update-rum-metric or delete-rum-metric. No prerequisites or exclusions are mentioned.

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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