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MCP-Airflow-API

MCP-Airflow-API

by call518
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_tasks

Retrieve all tasks in a specified DAG by exact DAG ID to inspect task structure and monitor workflows without direct Airflow API calls.

Instructions

[Tool Role]: Lists all tasks within the specified DAG.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dag_id": "<dag_id>"
    },
    "name": "list_tasks"
  }
}

Input Schema

NameRequiredDescriptionDefault
dag_idYesExact DAG identifier, as returned by list_dags; not its display name.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.7.4
    • addedInput schema / properties / dag_id / description
      Added value: +"Exact DAG identifier, as returned by list_dags; not its\ndisplay name."
  2. Addedv3.7.2
  3. Removedv3.6.5
  4. Changed2 schema fields changedv3.6.6
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / dag_id / title
      Removed value: -"Dag Id"
  5. First observed

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. 'Lists' implies a safe read operation, but the description says nothing about whether results are paginated, how they are ordered, or what happens for an unknown dag_id. It is adequate but thin for a zero-annotation tool.

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?

A single short sentence with the action and scope front-loaded, and no wasted words. It is perhaps over-compressed rather than verbose, but structurally clean.

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 required parameter, fully documented in the schema, and an output schema exists to explain return values. The description covers the essential scope, leaving only usage routing and pagination behavior unaddressed.

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?

Schema description coverage is 100%, and the schema already explains that dag_id is the exact identifier returned by list_dags, not a display name. The description's phrase 'specified DAG' adds nothing beyond that, so the baseline 3 applies.

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?

States a specific verb and resource (lists tasks) and scopes it to a single DAG, which implicitly distinguishes it from the sibling list_dags that enumerates DAGs themselves. It does not explicitly name any sibling, so it falls short of a 5.

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?

There is no guidance on when to use this versus alternatives such as get_dag, dag_graph, or list_task_instances_all. The only hint is the scope phrase 'within the specified DAG', which is implicit at best.

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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