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datadog-mcp-server

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create-logs-metric

Aggregate log events into count or distribution metrics. Apply filters and group-by fields to customize the metric from your logs.

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 provide safety context; description only states it creates a metric without disclosing side effects, permissions, rate limits, or whether it overwrites existing metrics. Minimal behavioral disclosure.

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 sentence with no wasted words, front-loading the core action and key options.

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?

For a creation tool with 6 parameters and no output schema, the description lacks details on return value, idempotency, or lifecycle. More context needed for full understanding.

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 has 100% description coverage, so baseline is 3. Description adds no new meaning beyond summarizing 'count or distribution, with filters and group-by'.

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 the verb 'create', resource 'metric based on log data', and specifies 'count or distribution, with filters and group-by', distinguishing it 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?

Description implies usage for log-based metrics but does not explicitly state when to use vs alternatives like create-rum-metric or provide when-not-to-use guidance.

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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