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datadog-mcp-server

create-logs-metric

Create a count or distribution metric from log data, applying filters and group-by fields for custom aggregation.

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?

Annotations indicate write operation (readOnlyHint=false) but description does not disclose potential side effects, idempotency, or concurrency behavior. No mention of authorization needs or resource limits.

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 that is front-loaded with key information. No unnecessary words, though could be slightly more structured with line breaks.

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?

Missing output schema, so description does not explain return values. While description covers aggregation types and filtering, it does not mention that 'path' is required for distribution metrics or detail groupBy structure. Adequate but not complete for a 6-parameter tool.

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 covers all 6 parameters with descriptions. Description adds a high-level summary ('count or distribution, with filters and group-by') but does not provide additional meaning beyond the schema. Baseline score for high coverage.

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', resource 'metric based on log data', and specifies the type (count or distribution) with filters and group-by, distinguishing it from sibling tools like create-rum-metric.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like create-rum-metric or create-spans-metric. Does not mention prerequisites or when not to use.

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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