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Apache Airflow MCP Server

by madamak

airflow_list_dag_runs

Read-onlyIdempotent

Retrieve and filter DAG runs from Apache Airflow with pagination, sorting, and direct UI links for monitoring workflow executions.

Instructions

List DAG runs (defaults to execution_date DESC) with per-run UI URLs.

Parameters

  • instance: Instance key (optional)

  • ui_url: Airflow UI URL to resolve instance/dag_id (optional)

  • dag_id: DAG identifier (required if ui_url not provided)

  • limit: Max results (default 100; accepts int/float/str, coerced to non-negative int, fractional values truncated)

  • offset: Offset for pagination (default 0; accepts int/float/str, coerced to non-negative int, fractional values truncated)

  • state: List of states to filter by (optional)

  • order_by: Optional "start_date", "end_date", or "execution_date" (omit to use execution_date)

  • descending: Sort direction (default True). Ignored when order_by is omitted; defaults always use execution_date descending

Returns

  • Response dict: { "dag_runs": [{ "dag_run_id", "state", "start_date", "end_date", "ui_url" }], "count": int, "request_id": str }

  • Raises: ToolError with compact JSON payload (code, message, request_id, optional context)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instanceNo
ui_urlNo
dag_idNo
limitNo
offsetNo
stateNo
order_byNo
descendingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety aspects. The description adds valuable behavioral context beyond annotations: it explains the default sorting behavior, parameter coercion rules (limit/offset accept multiple types), and error response format with ToolError details. This provides practical implementation guidance.

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 efficiently structured with clear sections (overview, Parameters, Returns). Every sentence earns its place by providing essential information about behavior, parameters, or outputs. No redundant information is present.

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?

Given the tool's complexity (8 parameters, filtering, sorting, pagination) and the presence of output schema, the description is complete. It covers all key aspects: purpose, parameter semantics, behavioral details, and return format. The output schema handles return value structure, so the description appropriately focuses on usage context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing comprehensive parameter documentation. It explains each parameter's purpose, requirements (dag_id vs ui_url), default values, type coercion behavior, and interaction rules (descending ignored when order_by omitted). This adds significant value beyond the bare schema.

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 ('List') and resource ('DAG runs'), specifies the default sorting behavior ('execution_date DESC'), and distinguishes from siblings by mentioning 'per-run UI URLs' which is unique among list operations. It precisely communicates what this tool does.

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 provides clear context about when to use this tool (listing DAG runs with UI URLs) and hints at parameter dependencies (dag_id required if ui_url not provided). However, it doesn't explicitly state when to choose this over alternatives like airflow_get_dag_run or airflow_list_task_instances, missing explicit sibling differentiation.

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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