AI Agent for Apache Airflow MCP
Seamlessly connect and manage Apache Airflow using the Model Context Protocol (MCP) server. This integration standardizes Airflow orchestration, enabling automated DAG, task, and resource management from MCP-compatible clients. Accelerate workflow automation, boost operational efficiency, and ensure robust compatibility with the official Apache Airflow client library.
Unified Airflow Workflow Management
Gain full control over Apache Airflow environments directly from MCP-enabled agents. Effortlessly manage DAGs, DAG runs, tasks, variables, connections, and more through standardized APIs. Centralize orchestration, simplify operations, and enable rapid workflow deployment at scale.
- Complete DAG Lifecycle Management.
- List, create, update, pause, unpause, and delete DAGs and their runs with full API coverage.
- Task and Variable Operations.
- Automate task management and variable handling for streamlined workflow execution and configuration.
- Secure Connections & Pools.
- Manage Airflow connections and resource pools securely, boosting scalability and reliability.
- Health & Monitoring APIs.
- Monitor Airflow health, stats, plugins, and logs for proactive issue resolution and compliance.
Flexible API Grouping & Read-Only Modes
Customize API exposure to match your compliance and security needs. Select specific Airflow API groups or enable read-only mode to restrict interactions to safe, non-destructive operations. Perfect for both production and sensitive environments.
- Read-Only Mode.
- Expose only GET/read operations for safe monitoring and auditing, ideal for compliance-sensitive environments.
- Custom API Group Selection.
- Enable or restrict access to Airflow APIs such as DAG, variable, eventlog, and more, tailored to your team’s requirements.
- Non-Destructive Testing.
- Test connections and fetch configuration data without altering workflow states.
Rapid Deployment & Easy Integration
Deploy your Airflow MCP server quickly with simple environment variables and flexible run options. Compatible with Claude Desktop, Smithery, and direct manual execution for smooth integration into any workflow automation stack.
- Instant Deployment.
- Deploy with a single command and environment variables, reducing setup time for development and production.
- Versatile Integration.
- Use with Claude Desktop, Smithery, or manual execution to fit any DevOps workflow.
MCP INTEGRATION
Available Apache Airflow MCP Integration Tools
The following tools are available as part of the Apache Airflow MCP integration:
- list_dags
List all available DAGs in the Apache Airflow instance.
- get_dag_details
Retrieve detailed information for a specific DAG.
- update_dag
Update the properties or configuration of an existing DAG.
- delete_dag
Delete a specified DAG from the Airflow instance.
- create_dag_run
Trigger a new run for a specified DAG.
- list_dag_runs
List all DAG runs for a specific DAG.
- get_dag_run_details
Fetch details of a specific DAG run.
- update_dag_run
Update the state or properties of a DAG run.
- delete_dag_run
Delete a specific DAG run from the Airflow instance.
- list_tasks
List all tasks defined in a specific DAG.
- get_task_details
Retrieve details for a specific task in a DAG.
- get_task_instance
Get information about a specific task instance in a DAG run.
- list_task_instances
List all task instances for a specific DAG run.
- update_task_instance
Update the state or details of a task instance.
- create_variable
Create a new Airflow variable.
- list_variables
List all Airflow variables.
- get_variable
Retrieve the value and details of a specific Airflow variable.
- update_variable
Update the value of an existing Airflow variable.
- delete_variable
Delete a specified Airflow variable.
- create_connection
Create a new Airflow connection.
- list_connections
List all configured Airflow connections.
- get_connection
Retrieve details for a specific Airflow connection.
- update_connection
Update the configuration of an existing Airflow connection.
- delete_connection
Delete a specified Airflow connection.
- test_connection
Test the connectivity for a specified Airflow connection.
- list_pools
List all resource pools in Airflow.
- create_pool
Create a new resource pool in Airflow.
- get_pool
Retrieve details of a specific Airflow pool.
- update_pool
Update the configuration of an existing Airflow pool.
- delete_pool
Delete a specified Airflow pool.
- list_xcoms
List all XCom entries for a specific task instance.
- get_xcom_entry
Retrieve a specific XCom entry by key.
- list_datasets
List all datasets registered in Airflow.
- get_dataset
Retrieve details of a specific dataset.
- create_dataset_event
Create a new dataset event in Airflow.
- list_event_logs
List all event logs in the Airflow instance.
- get_event_log
Retrieve details for a specific Airflow event log.
- get_config
Retrieve the Airflow instance configuration.
- get_health
Check the health status of the Airflow instance.
- get_plugins
Get the list of installed Airflow plugins.
- list_providers
List all providers installed in the Airflow instance.
- list_import_errors
List all import errors found in Airflow DAGs.
- get_import_error_details
Retrieve detailed information about a specific import error.
- get_version
Retrieve the version information of the Airflow instance.
Integrate Apache Airflow Seamlessly with MCP
Standardize and simplify your Airflow workflows using the Model Context Protocol. Book a live demo or try FlowHunt free to experience streamlined, secure orchestration through mcp-server-apache-airflow.
What is mcp-server-apache-airflow
mcp-server-apache-airflow is a Model Context Protocol (MCP) server implementation designed to seamlessly integrate Apache Airflow with MCP clients. This open-source project provides a standardized API for interacting with Apache Airflow, enabling users to manage, monitor, and control workflows (DAGs) programmatically. By wrapping Airflow's REST API, it simplifies integration with other systems, allowing organizations to manage their workflow orchestration environments in a unified, protocol-driven manner. Key features include listing, pausing, and unpausing DAGs, creating and managing DAG runs, and retrieving health status and version information. This project is ideal for developers and organizations looking to automate and standardize workflow processes across diverse infrastructures.
Capabilities
What we can do with mcp-server-apache-airflow
With mcp-server-apache-airflow, you can programmatically interact with Apache Airflow through a standardized protocol. This enables seamless integration for workflow management, automation, and monitoring. The service is ideal for connecting Airflow to other systems, DevOps pipelines, or AI agents, offering robust and flexible workflow orchestration.
- Standardized API Access
- Interact with Apache Airflow using a unified MCP API, reducing integration complexity.
- DAG Management
- List, pause, unpause, and control Directed Acyclic Graphs (DAGs) for flexible workflow orchestration.
- DAG Run Control
- Create, manage, and monitor DAG runs programmatically for automated workflow execution.
- Health and Version Checks
- Retrieve the health status and version of your Airflow instance easily.
- System Integration
- Integrate Airflow with other services and platforms using the Model Context Protocol for end-to-end automation.
How AI agents can benefit from mcp-server-apache-airflow
AI agents can leverage mcp-server-apache-airflow to automate complex workflow management tasks, monitor data pipelines, and trigger processes programmatically. By utilizing the standardized MCP interface, AI systems can efficiently orchestrate data processing, enhance workflow reliability, and enable seamless integration between machine learning models and production pipelines. This enhances operational efficiency and accelerates deployment cycles for AI-driven solutions.