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get_task_metrics

Access task-level execution metrics across workflows to analyze performance, application usage, and statistics for automation monitoring and optimization.

Instructions

Get all aggregate task metrics from the Workflow Engine.

Retrieves comprehensive task-level execution metrics across all workflows, providing detailed insights into task performance, application usage patterns, and execution statistics for automation monitoring and optimization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYesResponse model for task metrics collection endpoints. This Pydantic RootModel provides a standardized response format for API endpoints that return collections of task-level metrics from the Itential Platform workflow engine. It wraps a list of TaskMetricElement objects, enabling type-safe handling of task performance data across automation workflows. The root model pattern allows the response to be treated as a list while maintaining proper validation and serialization capabilities for the contained task metric elements, supporting comprehensive workflow monitoring and analysis. Args: root: List of TaskMetricElement objects containing task performance data. Defaults to an empty list if not provided. Attributes: root (List[TaskMetricElement]): Collection of task metric elements with application usage patterns and execution statistics for workflow analysis Example: Creating a response with multiple task metrics: >>> task1 = TaskMetricElement(taskType="automatic", name="validate-config", ... metrics=[], app="validator") >>> task2 = TaskMetricElement(taskType="manual", name="approve-deploy", ... metrics=[], app="approval-service") >>> response = GetTaskMetricsResponse([task1, task2]) >>> print(len(response.root)) # 2 >>> print(response.root[0].task_type) # "automatic" >>> print(response.root[1].name) # "approve-deploy" 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 TaskMetricElement objects - Enables filtering and analysis of tasks by application, workflow, or type
Behavior3/5

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

With no annotations provided, the description must carry the behavioral disclosure burden. It explains the type of data returned (task performance, application usage, execution statistics), but does not explicitly state the operation is read-only, nor does it mention any operational traits like aggregation timing or response characteristics. Since it is a getter with no params, the lack of side-effect warnings is less critical, but the description still leaves some behavioral details implicit.

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?

Two sentences with a direct lead and a clarifying second sentence. The second sentence includes some qualitative filler ('comprehensive', 'detailed insights'), but it also adds specific metric categories and use cases, keeping the description reasonably efficient.

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

Completeness4/5

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

For a zero-parameter aggregate metrics tool with an output schema, the description covers what it returns and its global scope. It does not explicitly discuss the distinction from scoped metric tools or potential query limitations, but these are not critical given the simple signature and available output schema.

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 there is nothing for the description to document beyond the empty schema. The description adds value by describing the categories of metrics returned, satisfying the baseline for parameterless tools.

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 opening sentence 'Get all aggregate task metrics from the Workflow Engine' clearly identifies a specific verb (get), resource (task metrics), and scope (all aggregate, across all workflows). This scope distinguishes it from sibling scoped variants like get_task_metrics_for_workflow or get_task_metrics_for_app.

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 states the tool returns metrics 'across all workflows', which provides clear contextual scope for when to use this aggregate view. It does not explicitly name alternatives or exclusions, but the global scope implies the distinction from the scoped sibling tools.

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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