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get_task_metrics_for_app

Retrieve task execution metrics for a specified application from Workflow Engine to analyze automation performance and identify workflow trends.

Instructions

Get all task metrics for the specified application from Workflow Engine.

Retrieves task execution metrics filtered by a specific application name, providing insights into how tasks performed by that application are executing across different workflows and automation processes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the application to retrieve task metrics for

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?

No annotations are provided, so the description carries the full burden. It describes the retrieval behavior (getting all task metrics) and the filtering scope, but does not disclose any additional traits such as permissions, rate limits, or side effects. As a 'get' operation, it is implicitly read-only, but lacks explicit safety disclosure. It adds some context beyond the name but not a full behavioral profile.

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: an initial clear statement and a brief expansion. Every word contributes to explaining the tool's purpose and scope. There is no redundancy or filler.

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?

The tool is simple (one parameter) and has an output schema, so the description does not need to explain return values. It explains what 'all task metrics' means and the application filter. It could be slightly more complete by explicitly naming alternative tools for clarity, but given the simple structure and available schema, it is sufficiently complete.

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?

Schema coverage is 100% (the 'name' parameter is described). The description adds further meaning by clarifying that this name refers to an application and that the tool returns all task metrics across workflows and automation processes. This goes slightly beyond the schema description, justifying a score above the baseline.

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 ('Get all task metrics'), the resource ('for the specified application'), and the context ('from Workflow Engine'). It explicitly says metrics are filtered by application name, which distinguishes it from sibling tools like get_task_metrics_for_workflow or get_task_metrics_for_task.

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 gives clear context for when to use this tool: when you need task metrics for a specific application. It does not explicitly mention alternatives or exclusions, but the filter by application name is a strong cue. This is more than implied usage, but not an explicit when/when-not.

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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