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Apache Airflow MCP Server

airflow_list_dag_runs

Read-onlyIdempotent

List DAG runs from Apache Airflow with filtering by state and sorting by date, including pagination and direct UI links to each run.

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", "execution_date", or "logical_date" (omit to use execution_date; execution_date and logical_date are mapped to whichever name the target Airflow version uses)

  • 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
limitNo
stateNo
dag_idNo
offsetNo
ui_urlNo
instanceNo
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 indicate readOnly, idempotent, non-destructive. Description adds significant behavioral details: default sorting, parameter coercion, dependency logic for instance/ui_url/dag_id, and error format. No contradictions with annotations.

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?

Well-structured with headings and bullet points. Slightly verbose but no redundant information. Each sentence adds value. Appropriate length for 8 parameters.

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?

Covers all parameters, default behavior, return format, and error handling. Given the presence of output schema in description and annotations, it is sufficiently complete. Could mention pagination or rate limits but not critical.

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?

Schema coverage is 0%, so description fully compensates. Every parameter is explained with defaults, coercion, optionality, and dependencies (e.g., dag_id required if ui_url not provided). Adds meaning beyond schema (e.g., fractional truncation, order_by mapping).

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?

Description clearly states it lists DAG runs with default ordering and includes UI URLs. It specifies the verb 'list' and resource 'DAG runs', and distinguishes from sibling tools like 'airflow_get_dag_run' (single run) and 'airflow_list_dags' (list DAGs).

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives (e.g., get_dag_run, list_dags). It does not state conditions or prerequisites, leaving the agent to infer usage from context.

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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