MCP-Airflow-API

get_task_instance_details

Retrieve detailed task instance information from Apache Airflow by specifying the DAG ID, run ID, and task ID. Access status, logs, and metadata for effective troubleshooting.

Instructions

[Tool Role]: Gets detailed information for a specific task instance.

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>",
      "task_id": "<task_id>"
    },
    "name": "get_task_instance_details"
  }
}

Input Schema

Name	Required	Description	Default
`dag_id`	Yes
`task_id`	Yes
`dag_run_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.0.0Jul 31, 2026

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read operation but does not disclose auth requirements, error behavior, or any other side effects. The description adds minimal context beyond the tool name.

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, clear sentence with no unnecessary words. It is appropriately front-loaded and avoids fluff, making it easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three required parameters and an output schema, the description is too sparse. It lacks usage context, fails to distinguish from similar sibling tools, and provides no guidance on how the parameters relate. The output schema covers return values, but overall completeness is insufficient.

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

Parameters1/5

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

Schema description coverage is 0%, so the description must compensate for missing parameter explanations. It does not mention any parameter details, adding no value beyond the parameter names themselves (dag_id, dag_run_id, task_id).

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?

The description clearly states the tool gets detailed information for a specific task instance, using a specific verb and resource. However, it does not distinguish this from sibling tools like get_task_instance_logs or list_task_instances_all, missing the opportunity to differentiate.

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 guidance is provided on when to use this tool versus alternatives. The description only states what it does without mentioning context, prerequisites, or exclusions.

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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