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MCP-Airflow-API

MCP-Airflow-API

by call518
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

dag_graph

Retrieve the task graph structure for a specified DAG to inspect task dependencies and workflow layout without direct Airflow API calls.

Instructions

[Tool Role]: Retrieves task graph structure for 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": "dag_graph"
  }
}

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?

With no annotations, the description carries the burden, and 'Retrieves' at least signals a read-only operation. It does not disclose anything about cost, size for large DAGs, permissions, or what 'graph structure' contains (nodes, edges, dependencies), though the presence of an output schema reduces the need to describe return values.

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 front-loaded sentence with no filler; the only redundancy is the '[Tool Role]:' label, which carries little information. Appropriately sized for a simple one-parameter read.

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?

The output schema and 100% parameter coverage mean the essentials are covered, but for a tool with many closely related siblings, the description leaves the agent without a selection criterion. Naming the alternative (get_dag for metadata, list_tasks for task list) would close the gap.

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 coverage is 100% and the single parameter is well documented, including the important 'not its display name' caveat, so the baseline of 3 applies. The description adds nothing beyond the schema.

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 ('Retrieves') and resource ('task graph structure for the specified DAG'), which is meaningfully different from get_dag or list_tasks. It does not explicitly name or contrast those siblings, so the differentiation must be inferred.

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 statement of when to use this instead of get_dag, list_tasks, or dag_code, all of which appear in the sibling list and sound related. An agent has to guess whether it wants the graph topology or DAG metadata.

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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