Skip to main content
Glama
itential

Itential MCP Server

Official
by itential

Get Task Metrics for App

get_task_metrics_for_app
Read-onlyIdempotent

Retrieve task execution metrics for a specific application from the Workflow Engine to gain insights into automation performance across workflows.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds that the tool reads from Workflow Engine and provides execution insights, but does not disclose any additional behavioral details such as error conditions, pagination, or the specific metrics returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, but the second sentence largely restates the first ('Retrieves task execution metrics filtered by a specific application name' is redundant with 'Get all task metrics for the specified application'), and the phrase 'providing insights...' adds little value.

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

Completeness3/5

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

With a single well-documented parameter and an output schema present, the description covers the basic invocation context. However, it lacks explicit usage guidance compared to its many similar sibling tools, and it does not clarify what the metrics contain or any edge cases.

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?

The single parameter 'name' is fully described in the schema (100% coverage), and the description repeats that the filter is by application name. No additional parameter semantics are provided beyond the schema, so the baseline of 3 applies.

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's function with a specific verb ('Get') and resource ('all task metrics') scoped by 'the specified application from Workflow Engine'. It distinguishes itself from sibling tools like get_task_metrics, get_task_metrics_for_task, and get_task_metrics_for_workflow by explicitly mentioning application filtering.

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 when needing task metrics filtered by application name, but it does not explicitly state when to use this tool versus the related siblings, nor does it mention when not to use it. Alternatives are not named, which is a gap given the many similar sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/itential/itential-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server