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Airflow MCP

Airflow MCP

by zamax14
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_task_instances

Retrieve all task instances and their current state for a specific DAG run in Airflow.

Instructions

List task instances and their state for a given DAG run.

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>",
      "dag_run_id": "<dag_run_id>"
    },
    "name": "list_task_instances"
  }
}

Input Schema

NameRequiredDescriptionDefault
dag_idYes
dag_run_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations, the description carries full burden but only states core function. It does not disclose behavioral traits such as read-only nature, pagination, rate limits, or any side effects. For a listing tool, this is insufficient.

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 a single focused sentence with no redundant information. It is appropriately short for a simple list operation.

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?

Given the tool's simplicity and presence of output schema, the description is minimally adequate. However, the lack of annotations and parameter descriptions leaves gaps in behavioral understanding. Additional context on output format or state values would improve completeness.

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

Parameters2/5

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

Schema coverage is 0%, so description must compensate. It only loosely ties parameters to 'for a given DAG run', implying 'dag_id' and 'dag_run_id' but adds no specific detail beyond the schema titles. Minimal value added.

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 the resource 'task instances' with a specific scope 'for a given DAG run'. It distinctly separates this tool from siblings like 'list_dag_runs' (different resource) and 'get_task_logs' (different operation).

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

Usage Guidelines3/5

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

The description implies usage when needing task instances for a specific DAG run, but it does not provide explicit when/when-not guidance or mention alternatives like 'get_task_logs' for logs. The context from sibling tool names partially compensates.

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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