Skip to main content
Glama
madamak

Apache Airflow MCP Server

airflow_get_task_instance

Read-onlyIdempotent

Get task instance details including metadata, config, attempt summary, and rendered fields by providing DAG ID, run ID, task ID, or a UI URL.

Instructions

Return task metadata, config, attempt summary, optional rendered fields, and UI URLs.

Parameters

  • instance | ui_url: Target selection (URL precedence)

  • dag_id, dag_run_id, task_id: Required identifiers (unless resolved from ui_url)

  • include_rendered: When true, include rendered template fields (truncated using max_rendered_bytes)

  • max_rendered_bytes: Byte cap for rendered fields payload (default 100KB; accepts int/float/str, coerced to positive int, fractional values truncated)

Returns

  • Response dict: { "task_instance": {...}, "task_config": {...}, "attempts": {...}, "ui_url": {...}, "request_id": str, "rendered_fields"?: {...} }

Notes

  • attempts.try_number is the authoritative input for airflow_get_task_instance_logs.

  • Rendered fields include bytes_returned and truncated metadata.

  • Sensors increment try_number on every reschedule, so treat it as an attempt index; the derived retries counters are heuristic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idNo
ui_urlNo
task_idNo
instanceNo
dag_run_idNo
include_renderedNo
max_rendered_bytesNo

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 safe read, and description adds details on optional rendering, truncation, and sensor try_number behavior. No contradictions.

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 summary, parameter list, return dict, and notes. Concise yet informative, though slightly lengthy.

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 parameter behavior and return structure adequately; output schema exists for full details. Leaves minor gaps (e.g., error cases), but overall complete.

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?

With schema coverage at 0%, description explains all 7 parameters, including target selection, required identifiers, and max_rendered_bytes coercion. Provides full context.

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 'Return task metadata, config, attempt summary, optional rendered fields, and UI URLs.' It identifies the specific resource (task instance) and distinguishes from sibling list/other tools.

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?

Provides parameter guidance (URL precedence, required identifiers) and mentions the sibling tool for logs, but lacks explicit when-to-use vs. alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/madamak/apache-airflow-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server