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antonio-mello-ai

mcp-airflow

by antonio-mello-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
AIRFLOW_BASE_URLYesAirflow REST API base URL. Use /api/v2 for Airflow 3.x or /api/v1 for 2.x
AIRFLOW_PASSWORDYesAuth password
AIRFLOW_USERNAMEYesAuth username (JWT on 3.x, basic auth on 2.x)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_dagsA

List all DAGs with their paused/active status.

Returns a summary of every DAG registered in Airflow, including
the dag_id, whether it is paused, and whether it is active.
get_dag_runs_todayA

Get all DAG runs from today with their status.

Returns every DAG run that started on the current UTC date, grouped by status (success, failed, running, queued).

get_dag_run_statusA

Get the latest run status for a specific DAG.

Args:
    dag_id: The DAG identifier.

Returns the state, execution date, and duration of the most recent run.
check_failed_dagsA

Check for failed DAG runs in the last 24 hours.

Returns a list of DAGs that had at least one failed run in the past 24 hours, with execution dates.

check_scheduler_healthA

Check the Airflow scheduler health via the /health endpoint.

Returns the scheduler status and latest heartbeat timestamp.

trigger_dag_runA

Trigger a manual DAG run.

Args:
    dag_id: The DAG identifier to trigger.

Returns confirmation with the new run ID and state.
get_task_instancesA

Get individual task instances for a specific DAG run.

Args:
    dag_id: The DAG identifier.
    run_id: The DAG run identifier.

Returns the list of tasks with their state, duration, and operator.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.2/5.0

Scored across 7 tools

Disambiguation5/5

Every tool targets a distinct aspect of Airflow (failed DAGs, scheduler health, specific DAG run status, today's runs, task instances, DAG listing, manual trigger). No two tools overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with lowercase and underscores (e.g., check_failed_dags, get_dag_run_status). No deviations or mixed conventions.

Tool Count5/5

7 tools is well-scoped for an Airflow monitoring/management server. It covers essential operations without being excessive or minimal.

Completeness4/5

Covers core monitoring (health, DAG run status, task instances) and triggering. Missing pause/unpause, DAG details, or run deletion, but these are secondary for typical agent workflows.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources