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datadog-mcp-server

create-logs-metric

Create a metric from log data using count or distribution aggregation, with filters and group-by fields for precise measurement.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe name of the log-based metric. Example: logs.status_code_count
aggregationTypeYesAggregation type. 'count' counts log events, 'distribution' creates a distribution metric
pathNoPath to the metric value. Required for distribution metrics. Example: @duration
includePercentilesNoWhether to include percentile aggregations. Only for distribution metrics
filterQueryNoLog search query to filter events. Example: service:web-app status:error
groupByNoFields to group the metric by
Behavior2/5

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

No annotations provided, so description carries burden. Only describes basic operation (create) but no side effects, idempotency, permissions, or error conditions. Minimal disclosure.

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?

Single sentence, 15 words, very concise. Front-loads main purpose. Could include more detail without being overly verbose.

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

Completeness3/5

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

No output schema. Description covers basic creation but lacks information on return values, error states, or how the metric ID works. Adequate but gaps remain for a 6-parameter 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?

Schema has 100% coverage with descriptions. The description adds value by summarizing key parameters (count, distribution, filters, group-by) and mentioning path for distribution, which clarifies the aggregation type parameter.

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?

Description clearly states it creates a log-based metric with two aggregation types (count/distribution) and optional filters/group-by. Distinguishes from siblings like create-rum-metric or create-spans-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?

Provides implied usage (for log metrics) but no explicit when-to-use or when-not-to-use. Alternatives like update-logs-metric exist but no guidance on which to choose.

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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