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MCP Server for Apache Airflow

by yangkyeongmo
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mcp-server-apache-airflow

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A Model Context Protocol (MCP) server implementation for Apache Airflow, enabling seamless integration with MCP clients. This project provides a standardized way to interact with Apache Airflow through the Model Context Protocol.

About

This project implements a Model Context Protocol server that wraps Apache Airflow's REST API, allowing MCP clients to interact with Airflow in a standardized way. It uses the official Apache Airflow client library to ensure compatibility and maintainability.

Related MCP server: MCP Server for Apache Airflow

Feature Implementation Status

Feature

API Path

Status

DAG Management

List DAGs

/api/v1/dags

✅

Get DAG Details

/api/v1/dags/{dag_id}

✅

Pause DAG

/api/v1/dags/{dag_id}

✅

Unpause DAG

/api/v1/dags/{dag_id}

✅

Update DAG

/api/v1/dags/{dag_id}

✅

Delete DAG

/api/v1/dags/{dag_id}

✅

Get DAG Source

/api/v1/dagSources/{file_token}

✅

Patch Multiple DAGs

/api/v1/dags

✅

Reparse DAG File

/api/v1/dagSources/{file_token}/reparse

✅

DAG Runs

List DAG Runs

/api/v1/dags/{dag_id}/dagRuns

✅

Create DAG Run

/api/v1/dags/{dag_id}/dagRuns

✅

Get DAG Run Details

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}

✅

Update DAG Run

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}

✅

Delete DAG Run

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}

✅

Get DAG Runs Batch

/api/v1/dags/~/dagRuns/list

✅

Clear DAG Run

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/clear

✅

Set DAG Run Note

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/setNote

✅

Get Upstream Dataset Events

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/upstreamDatasetEvents

✅

Tasks

List DAG Tasks

/api/v1/dags/{dag_id}/tasks

✅

Get Task Details

/api/v1/dags/{dag_id}/tasks/{task_id}

✅

Get Task Instance

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}

✅

List Task Instances

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances

✅

Update Task Instance

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}

✅

Get Task Instance Log

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/logs/{task_try_number}

✅

Clear Task Instances

/api/v1/dags/{dag_id}/clearTaskInstances

✅

Set Task Instances State

/api/v1/dags/{dag_id}/updateTaskInstancesState

✅

List Task Instance Tries

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/tries

✅

Variables

List Variables

/api/v1/variables

✅

Create Variable

/api/v1/variables

✅

Get Variable

/api/v1/variables/{variable_key}

✅

Update Variable

/api/v1/variables/{variable_key}

✅

Delete Variable

/api/v1/variables/{variable_key}

✅

Connections

List Connections

/api/v1/connections

✅

Create Connection

/api/v1/connections

✅

Get Connection

/api/v1/connections/{connection_id}

✅

Update Connection

/api/v1/connections/{connection_id}

✅

Delete Connection

/api/v1/connections/{connection_id}

✅

Test Connection

/api/v1/connections/test

✅

Pools

List Pools

/api/v1/pools

✅

Create Pool

/api/v1/pools

✅

Get Pool

/api/v1/pools/{pool_name}

✅

Update Pool

/api/v1/pools/{pool_name}

✅

Delete Pool

/api/v1/pools/{pool_name}

✅

XComs

List XComs

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/xcomEntries

✅

Get XCom Entry

/api/v1/dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/xcomEntries/{xcom_key}

✅

Datasets

List Datasets

/api/v1/datasets

✅

Get Dataset

/api/v1/datasets/{uri}

✅

Get Dataset Events

/api/v1/datasetEvents

✅

Create Dataset Event

/api/v1/datasetEvents

✅

Get DAG Dataset Queued Event

/api/v1/dags/{dag_id}/dagRuns/queued/datasetEvents/{uri}

✅

Get DAG Dataset Queued Events

/api/v1/dags/{dag_id}/dagRuns/queued/datasetEvents

✅

Delete DAG Dataset Queued Event

/api/v1/dags/{dag_id}/dagRuns/queued/datasetEvents/{uri}

✅

Delete DAG Dataset Queued Events

/api/v1/dags/{dag_id}/dagRuns/queued/datasetEvents

✅

Get Dataset Queued Events

/api/v1/datasets/{uri}/dagRuns/queued/datasetEvents

✅

Delete Dataset Queued Events

/api/v1/datasets/{uri}/dagRuns/queued/datasetEvents

✅

Monitoring

Get Health

/api/v1/health

✅

DAG Stats

Get DAG Stats

/api/v1/dags/statistics

✅

Config

Get Config

/api/v1/config

✅

Plugins

Get Plugins

/api/v1/plugins

✅

Providers

List Providers

/api/v1/providers

✅

Event Logs

List Event Logs

/api/v1/eventLogs

✅

Get Event Log

/api/v1/eventLogs/{event_log_id}

✅

System

Get Import Errors

/api/v1/importErrors

✅

Get Import Error Details

/api/v1/importErrors/{import_error_id}

✅

Get Health Status

/api/v1/health

✅

Get Version

/api/v1/version

✅

Setup

Dependencies

This project depends on the official Apache Airflow client library (apache-airflow-client). It will be automatically installed when you install this package.

Environment Variables

Set the following environment variables:

AIRFLOW_HOST=<your-airflow-host> # Optional, defaults to http://localhost:8080 AIRFLOW_API_VERSION=v1 # Optional, defaults to v1 READ_ONLY=true # Optional, enables read-only mode (true/false, defaults to false)

Authentication

Choose one of the following authentication methods:

Basic Authentication (default):

AIRFLOW_USERNAME=<your-airflow-username> AIRFLOW_PASSWORD=<your-airflow-password>

JWT Token Authentication:

AIRFLOW_JWT_TOKEN=<your-jwt-token>

To obtain a JWT token, you can use Airflow's authentication endpoint:

ENDPOINT_URL="http://localhost:8080" # Replace with your Airflow endpoint curl -X 'POST' \ "${ENDPOINT_URL}/auth/token" \ -H 'Content-Type: application/json' \ -d '{ "username": "<your-username>", "password": "<your-password>" }'

Note: If both JWT token and basic authentication credentials are provided, JWT token takes precedence.

Usage with Claude Desktop

Add to your claude_desktop_config.json:

Basic Authentication:

{ "mcpServers": { "mcp-server-apache-airflow": { "command": "uvx", "args": ["mcp-server-apache-airflow"], "env": { "AIRFLOW_HOST": "https://your-airflow-host", "AIRFLOW_USERNAME": "your-username", "AIRFLOW_PASSWORD": "your-password" } } } }

JWT Token Authentication:

{ "mcpServers": { "mcp-server-apache-airflow": { "command": "uvx", "args": ["mcp-server-apache-airflow"], "env": { "AIRFLOW_HOST": "https://your-airflow-host", "AIRFLOW_JWT_TOKEN": "your-jwt-token" } } } }

For read-only mode (recommended for safety):

Basic Authentication:

{ "mcpServers": { "mcp-server-apache-airflow": { "command": "uvx", "args": ["mcp-server-apache-airflow"], "env": { "AIRFLOW_HOST": "https://your-airflow-host", "AIRFLOW_USERNAME": "your-username", "AIRFLOW_PASSWORD": "your-password", "READ_ONLY": "true" } } } }

JWT Token Authentication:

{ "mcpServers": { "mcp-server-apache-airflow": { "command": "uvx", "args": ["mcp-server-apache-airflow", "--read-only"], "env": { "AIRFLOW_HOST": "https://your-airflow-host", "AIRFLOW_JWT_TOKEN": "your-jwt-token" } } } }

Alternative configuration using uv:

Basic Authentication:

{ "mcpServers": { "mcp-server-apache-airflow": { "command": "uv", "args": [ "--directory", "/path/to/mcp-server-apache-airflow", "run", "mcp-server-apache-airflow" ], "env": { "AIRFLOW_HOST": "https://your-airflow-host", "AIRFLOW_USERNAME": "your-username", "AIRFLOW_PASSWORD": "your-password" } } } }

JWT Token Authentication:

{ "mcpServers": { "mcp-server-apache-airflow": { "command": "uv", "args": [ "--directory", "/path/to/mcp-server-apache-airflow", "run", "mcp-server-apache-airflow" ], "env": { "AIRFLOW_HOST": "https://your-airflow-host", "AIRFLOW_JWT_TOKEN": "your-jwt-token" } } } }

Replace /path/to/mcp-server-apache-airflow with the actual path where you've cloned the repository.

Selecting the API groups

You can select the API groups you want to use by setting the --apis flag.

uv run mcp-server-apache-airflow --apis dag --apis dagrun

The default is to use all APIs.

Allowed values are:

  • config

  • connections

  • dag

  • dagrun

  • dagstats

  • dataset

  • eventlog

  • importerror

  • monitoring

  • plugin

  • pool

  • provider

  • taskinstance

  • variable

  • xcom

Read-Only Mode

You can run the server in read-only mode by using the --read-only flag or by setting the READ_ONLY=true environment variable. This will only expose tools that perform read operations (GET requests) and exclude any tools that create, update, or delete resources.

Using the command-line flag:

uv run mcp-server-apache-airflow --read-only

Using the environment variable:

READ_ONLY=true uv run mcp-server-apache-airflow

In read-only mode, the server will only expose tools like:

  • Listing DAGs, DAG runs, tasks, variables, connections, etc.

  • Getting details of specific resources

  • Reading configurations and monitoring information

  • Testing connections (non-destructive)

Write operations like creating, updating, deleting DAGs, variables, connections, triggering DAG runs, etc. will not be available in read-only mode.

You can combine read-only mode with API group selection:

uv run mcp-server-apache-airflow --read-only --apis dag --apis variable

Manual Execution

You can also run the server manually:

make run

make run accepts following options:

Options:

  • --port: Port to listen on for SSE (default: 8000)

  • --transport: Transport type (stdio/sse/http, default: stdio)

Or, you could run the sse server directly, which accepts same parameters:

make run-sse

Also, you could start service directly using uv like in the following command:

uv run src --transport http --port 8080

Installing via Smithery

To install Apache Airflow MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @yangkyeongmo/mcp-server-apache-airflow --client claude

Development

Setting up Development Environment

  1. Clone the repository:

git clone https://github.com/yangkyeongmo/mcp-server-apache-airflow.git cd mcp-server-apache-airflow

  1. Install development dependencies:

uv sync --dev

  1. Create a .env file for environment variables (optional for development):

touch .env

Note: No environment variables are required for running tests. The AIRFLOW_HOST defaults to http://localhost:8080 for development and testing purposes.

Running Tests

The project uses pytest for testing with the following commands available:

# Run all tests make test

Code Quality

# Run linting make lint # Run code formatting make format

Continuous Integration

The project includes a GitHub Actions workflow (.github/workflows/test.yml) that automatically:

  • Runs tests on Python 3.10, 3.11, and 3.12

  • Executes linting checks using ruff

  • Runs on every push and pull request to main branch

The CI pipeline ensures code quality and compatibility across supported Python versions before any changes are merged.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

The package is deployed automatically to PyPI when project.version is updated in pyproject.toml. Follow semver for versioning.

Please include version update in the PR in order to apply the changes to core logic.

License

MIT License

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security – no known vulnerabilities
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