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Get Job Metrics

get_job_metrics
Read-onlyIdempotent

Retrieve aggregate workflow job metrics to assess automation efficiency, success rates, and resource utilization across all executed jobs.

Instructions

Get aggregate job metrics from the Workflow Engine.

The Workflow Engine maintains comprehensive metrics about workflow execution performance, providing insights into automation efficiency, success rates, and resource utilization across all workflow jobs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYesResponse model for job metrics collection endpoints. This Pydantic RootModel provides a standardized response format for API endpoints that return collections of job metrics from the Itential Platform workflow engine. It wraps a list of JobMetricElement objects, enabling type-safe handling of job metrics data across the platform. The root model pattern allows the response to be treated as a list while maintaining proper validation and serialization capabilities for the contained job metric elements. Args: root: List of JobMetricElement objects containing job performance data. Defaults to an empty list if not provided. Attributes: root (List[JobMetricElement]): Collection of job metric elements with performance and completion statistics for workflow monitoring Example: Creating a response with multiple job metrics: >>> job1 = JobMetricElement(_id="job1", workflow="wf1", metrics=[], ... jobsComplete=10, totalRunTime=100.0) >>> job2 = JobMetricElement(_id="job2", workflow="wf2", metrics=[], ... jobsComplete=20, totalRunTime=250.5) >>> response = GetJobMetricsResponse([job1, job2]) >>> print(len(response.root)) # 2 >>> print(response.root[0].jobs_complete) # 10 Notes: - Uses default_factory=list to create empty collections when needed - Supports iteration and indexing through the root attribute - Maintains type safety for all contained JobMetricElement objects
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds meaningful context about the nature of the metrics (automation efficiency, success rates, resource utilization), which goes beyond the structured data.

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 two sentences, front-loaded with the main action, and adds relevant detail in the second sentence. Every word earns its place with no redundancy or filler.

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

Completeness5/5

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

For a no-parameter, read-only tool with an output schema, the description is complete. It states the source and scope of the metrics, and the annotations plus output schema cover the rest. No additional behavior needs disclosure.

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?

The tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and the schema coverage is 100% with no param descriptions required.

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 gets aggregate job metrics from the Workflow Engine, with a specific verb and resource. It distinguishes from siblings like get_task_metrics and get_job_metrics_for_workflow by emphasizing 'aggregate' and 'across all workflow jobs'.

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 overall job metrics but provides no explicit guidance on when to use this versus alternatives such as get_job_metrics_for_workflow or get_task_metrics. It does not mention alternatives or exclusions.

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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