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Get Job Metrics for Workflow

get_job_metrics_for_workflow
Read-onlyIdempotent

Retrieve job execution metrics for a specified workflow to gain targeted insights into performance and execution statistics.

Instructions

Get the job metrics for the specified workflow from Workflow Engine.

Retrieves job execution metrics filtered by a specific workflow name, providing targeted insights into the performance and execution statistics for jobs within that particular workflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the workflow to get the job metrics for

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
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety and idempotency. The description adds some context about returning 'job execution metrics' and 'performance and execution statistics', but doesn't disclose additional behavioral traits like pagination, rate limits, or error conditions. With annotations present, this is adequate but not rich.

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 concise—two sentences—with the main action front-loaded. The second sentence elaborates on the purpose without unnecessary fluff, making it efficient and well-structured.

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 simple one-parameter tool with rich annotations, a read-only hint, and an output schema, the description is complete. It clearly states what the tool does and the filtering criterion; the output schema handles return values, so no further detail is needed.

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 description coverage is 100% and the only parameter 'name' is fully documented in the schema. The description repeats the concept of 'specified workflow name' but adds no extra semantic detail beyond the schema, so the baseline of 3 is appropriate.

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 uses a specific verb 'Get' with a clear resource 'job metrics' scoped to 'the specified workflow', distinguishing it from sibling tools like get_job_metrics (global) and get_task_metrics_for_workflow (task metrics). This makes the tool's purpose unambiguous.

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

Usage Guidelines4/5

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

The description clearly implies the tool is for retrieving job metrics filtered by a specific workflow name, providing clear usage context. However, it does not explicitly name alternatives or state when not to use it, lacking the 'when-not/alternatives' threshold for a 5.

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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