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list_pipeline_runs

List Azure Data Factory pipeline runs within a specified time window, filter by pipeline name or status, and retrieve run details for monitoring.

Instructions

List pipeline runs in a time window (default: last 24 hours).

Time values are ISO 8601 strings, e.g. "2026-08-22T00:00:00Z". Narrow results with pipeline_name and/or status (e.g. "Failed", "Succeeded", "InProgress"). Run messages are truncated to 500 chars here - use get_pipeline_run with a run_id from this result for full detail.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNo
end_timeNo
start_timeNo
factory_nameYes
pipeline_nameNo
resource_groupYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It reveals the default time window (last 24 hours), specifies ISO 8601 time formats, and discloses message truncation. It doesn't mention pagination, sorting, or whether results are limited, but for a read-only list operation with an output schema, the disclosed traits are significant and useful.

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?

Three sentences with no redundancy. Purpose and default are up front; filters and format follow; truncation and alternative are saved for the end. Every sentence earns its place and the structure is tight.

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 description covers the core behavior (listing runs, filters, default window, truncation) and points to get_pipeline_run for full detail. An output schema exists, so return structure is handled elsewhere. It omits pagination or sorting details, but those may be in the output schema or are secondary for a list operation. Overall, sufficient for correct invocation.

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 description coverage is 0%, so the description must compensate. It explains start_time/end_time format, gives status examples, and describes pipeline_name as a filter. It doesn't explain resource_group/factory_name, but they are required and contextually obvious (factory and resource group). The description adds real meaning beyond the raw schema, though not exhaustive.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists pipeline runs within a time window, including a default window, and mentions filtering by name and status. It distinguishes itself from get_pipeline_run (which retrieves a single run) but doesn't explicitly contrast with list_pipelines or list_activity_runs, so it's clear but not sharply differentiated from all siblings.

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

Usage Guidelines5/5

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

The description explicitly says run messages are truncated to 500 chars and directs the agent to use get_pipeline_run with a run_id for full detail—a clear when-to-use-alternative instruction. It also implies this is the tool for listing runs, not for retrieving individual run details. No ambiguity about selection.

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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