MCP Server for Apache Airflow
The MCP Server for Apache Airflow provides a standardized interface that allows MCP clients to interact with Apache Airflow through the Model Context Protocol. With this server, you can:
DAG Management: List, retrieve, pause, unpause, update, delete DAGs; manage DAG sources and reparse DAG files
DAG Runs: List, create, retrieve, update, delete, clear, and set notes for DAG runs; manage upstream dataset events
Tasks: Manage tasks and task instances including listing, retrieving, updating, clearing, and setting states
Resources: Create, list, update, and delete variables, connections, and pools
Data Exchange: List and retrieve XComs; manage datasets and dataset events
Monitoring: Check health status, retrieve DAG statistics, view configuration and system information
System Info: Access plugins, providers, event logs, import errors, and version information
Allows interaction with Apache Airflow through a Model Context Protocol server that wraps Airflow's REST API. Supports DAG management (listing, viewing details, pausing/unpausing), DAG runs (listing, creating), task management (listing tasks and task instances), and system information (health status, version, import errors).
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Server for Apache Airflow show me the status of the data_pipeline DAG"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-server-apache-airflow
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: astro-airflow-mcp
Feature Implementation Status
Feature | API Path | Status |
DAG Management | ||
List DAGs |
| ✅ |
Get DAG Details |
| ✅ |
Pause DAG |
| ✅ |
Unpause DAG |
| ✅ |
Update DAG |
| ✅ |
Delete DAG |
| ✅ |
Get DAG Source |
| ✅ |
Patch Multiple DAGs |
| ✅ |
Reparse DAG File |
| ✅ |
DAG Runs | ||
List DAG Runs |
| ✅ |
Create DAG Run |
| ✅ |
Get DAG Run Details |
| ✅ |
Update DAG Run |
| ✅ |
Delete DAG Run |
| ✅ |
Get DAG Runs Batch |
| ✅ |
Clear DAG Run |
| ✅ |
Set DAG Run Note |
| ✅ |
Get Upstream Dataset Events |
| ✅ |
Tasks | ||
List DAG Tasks |
| ✅ |
Get Task Details |
| ✅ |
Get Task Instance |
| ✅ |
List Task Instances |
| ✅ |
Update Task Instance |
| ✅ |
Get Task Instance Log |
| ✅ |
Clear Task Instances |
| ✅ |
Set Task Instances State |
| ✅ |
List Task Instance Tries |
| ✅ |
Variables | ||
List Variables |
| ✅ |
Create Variable |
| ✅ |
Get Variable |
| ✅ |
Update Variable |
| ✅ |
Delete Variable |
| ✅ |
Connections | ||
List Connections |
| ✅ |
Create Connection |
| ✅ |
Get Connection |
| ✅ |
Update Connection |
| ✅ |
Delete Connection |
| ✅ |
Test Connection |
| ✅ |
Pools | ||
List Pools |
| ✅ |
Create Pool |
| ✅ |
Get Pool |
| ✅ |
Update Pool |
| ✅ |
Delete Pool |
| ✅ |
XComs | ||
List XComs |
| ✅ |
Get XCom Entry |
| ✅ |
Datasets | ||
List Datasets |
| ✅ |
Get Dataset |
| ✅ |
Get Dataset Events |
| ✅ |
Create Dataset Event |
| ✅ |
Get DAG Dataset Queued Event |
| ✅ |
Get DAG Dataset Queued Events |
| ✅ |
Delete DAG Dataset Queued Event |
| ✅ |
Delete DAG Dataset Queued Events |
| ✅ |
Get Dataset Queued Events |
| ✅ |
Delete Dataset Queued Events |
| ✅ |
Monitoring | ||
Get Health |
| ✅ |
DAG Stats | ||
Get DAG Stats |
| ✅ |
Config | ||
Get Config |
| ✅ |
Plugins | ||
Get Plugins |
| ✅ |
Providers | ||
List Providers |
| ✅ |
Event Logs | ||
List Event Logs |
| ✅ |
Get Event Log |
| ✅ |
System | ||
Get Import Errors |
| ✅ |
Get Import Error Details |
| ✅ |
Get Health Status |
| ✅ |
Get 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 dagrunThe 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-onlyUsing the environment variable:
READ_ONLY=true uv run mcp-server-apache-airflowIn 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 variableManual Execution
You can also run the server manually:
make runmake 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-sseAlso, you could start service directly using uv like in the following command:
uv run src --transport http --port 8080Installing 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 claudeDevelopment
Setting up Development Environment
Clone the repository:
git clone https://github.com/yangkyeongmo/mcp-server-apache-airflow.git
cd mcp-server-apache-airflowInstall development dependencies:
uv sync --devCreate a
.envfile for environment variables (optional for development):
touch .envNote: No environment variables are required for running tests. The
AIRFLOW_HOSTdefaults tohttp://localhost:8080for development and testing purposes.
Running Tests
The project uses pytest for testing with the following commands available:
# Run all tests
make testCode Quality
# Run linting
make lint
# Run code formatting
make formatContinuous 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
mainbranch
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
Available Tools
68 toolsclear_dag_runD
Clear a DAG run
Inputs: dag_id (required), dag_run_id (required), dry_run.
clear_task_instancesD
Clear a set of task instances
Inputs: dag_id (required), task_ids, start_date, end_date, +8 more.
create_connectionD
Create a connection
Inputs: conn_id (required), conn_type (required), host, port, +4 more.
create_dataset_eventD
Create dataset event
Inputs: dataset_uri (required), extra.
create_variableD
Create a variable
Inputs: key (required), value (required), description.
delete_connectionC
Delete a connection by ID
Inputs: conn_id (required).
delete_dagD
Delete a DAG
Inputs: dag_id (required).
delete_dag_dataset_queued_eventC
Delete a queued Dataset event for a DAG
Inputs: dag_id (required), uri (required).
delete_dag_dataset_queued_eventsC
Delete queued Dataset events for a DAG
Inputs: dag_id (required), before.
delete_dag_runC
Delete a DAG run by DAG ID and DAG run ID
Inputs: dag_id (required), dag_run_id (required).
delete_dataset_queued_eventsC
Delete queued Dataset events for a Dataset
Inputs: uri (required), before.
delete_poolD
Delete a pool
Inputs: pool_name (required).
delete_variableC
Delete a variable by key
Inputs: key (required).
fetch_dagsC
Fetch all DAGs
Inputs: limit, offset, order_by, tags, +3 more.
get_configD
Get current configuration
Inputs: section.
get_connectionC
Get a connection by ID
Inputs: conn_id (required).
get_dagC
Get a DAG by ID
Inputs: dag_id (required).
get_dag_dataset_queued_eventC
Get a queued Dataset event for a DAG
Inputs: dag_id (required), uri (required).
get_dag_dataset_queued_eventsC
Get queued Dataset events for a DAG
Inputs: dag_id (required).
get_dag_detailsC
Get a simplified representation of DAG
Inputs: dag_id (required), fields.
get_dag_runC
Get a DAG run by DAG ID and DAG run ID
Inputs: dag_id (required), dag_run_id (required).
get_dag_runsD
Get DAG runs by ID
Inputs: dag_id (required), limit, offset, execution_date_gte, +9 more.
get_dag_runs_batchD
List DAG runs (batch)
Inputs: dag_ids, execution_date_gte, execution_date_lte, start_date_gte, +7 more.
get_dag_sourceD
Get a source code
Inputs: file_token (required).
get_dag_statsD
Get DAG stats
Inputs: dag_ids.
get_dag_tasksD
Get tasks for DAG
Inputs: dag_id (required).
get_datasetC
Get a dataset by URI
Inputs: uri (required).
get_dataset_eventsD
Get dataset events
Inputs: limit, offset, order_by, dataset_id, +4 more.
get_dataset_queued_eventsC
Get queued Dataset events for a Dataset
Inputs: uri (required).
get_datasetsC
List datasets
Inputs: limit, offset, order_by, uri_pattern, +1 more.
get_event_logC
Get a specific log entry by ID
Inputs: event_log_id (required).
get_event_logsC
List log entries from event log
Inputs: limit, offset, order_by, dag_id, +10 more.
get_healthC
Get instance status
Inputs: no named parameters.
get_import_errorC
Get a specific import error by ID
Inputs: import_error_id (required).
get_import_errorsC
List import errors
Inputs: limit, offset, order_by.
get_logC
Get the log from a task instance by DAG ID, task ID, DAG run ID and task try number
Inputs: dag_id (required), task_id (required), dag_run_id (required), task_try_number (required).
get_pluginsC
Get a list of loaded plugins
Inputs: limit, offset.
get_poolC
Get a pool by name
Inputs: pool_name (required).
get_poolsD
List pools
Inputs: limit, offset, order_by.
get_providersC
Get a list of loaded providers
Inputs: limit, offset.
get_taskC
Get a task by ID
Inputs: dag_id (required), task_id (required).
get_task_instanceC
Get a task instance by DAG ID, task ID, and DAG run ID
Inputs: dag_id (required), task_id (required), dag_run_id (required).
get_tasksD
Get tasks for DAG
Inputs: dag_id (required), order_by.
get_upstream_dataset_eventsC
Get dataset events for a DAG run
Inputs: dag_id (required), dag_run_id (required).
get_valueC
Get a specific option from configuration
Inputs: section (required), option (required).
get_variableD
Get a variable by key
Inputs: key (required).
get_versionC
Get version information
Inputs: no named parameters.
get_xcom_entriesC
Get all XCom entries
Inputs: dag_id (required), dag_run_id (required), task_id (required), map_index, +3 more.
get_xcom_entryD
Get an XCom entry
Inputs: dag_id (required), dag_run_id (required), task_id (required), xcom_key (required), +3 more.
list_connectionsC
List all connections
Inputs: limit, offset, order_by.
list_task_instancesC
List task instances by DAG ID and DAG run ID
Inputs: dag_id (required), dag_run_id (required), execution_date_gte, execution_date_lte, +13 more.
list_task_instance_triesC
List task instance tries by DAG ID, DAG run ID, and task ID
Inputs: dag_id (required), dag_run_id (required), task_id (required), limit, +2 more.
list_variablesD
List all variables
Inputs: limit, offset, order_by.
patch_dagD
Update a DAG
Inputs: dag_id (required), is_paused, tags.
patch_dagsC
Update multiple DAGs
Inputs: dag_id_pattern, is_paused, tags.
patch_poolD
Update a pool
Inputs: pool_name (required), slots, description, include_deferred.
pause_dagC
Pause a DAG by ID
Inputs: dag_id (required).
post_dag_runC
Trigger a DAG by ID
Inputs: dag_id (required), dag_run_id, data_interval_end, data_interval_start, +3 more.
post_poolC
Create a pool
Inputs: name (required), slots (required), description, include_deferred.
reparse_dag_fileC
Request re-parsing of a DAG file
Inputs: file_token (required).
set_dag_run_noteC
Update the DagRun note
Inputs: dag_id (required), dag_run_id (required), note (required).
set_task_instances_stateD
Set a state of task instances
Inputs: dag_id (required), state (required), task_ids, execution_date, +5 more.
test_connectionD
Test a connection
Inputs: conn_type (required), host, port, login, +3 more.
unpause_dagC
Unpause a DAG by ID
Inputs: dag_id (required).
update_connectionC
Update a connection by ID
Inputs: conn_id (required), conn_type, host, port, +4 more.
update_dag_run_stateC
Update a DAG run state by DAG ID and DAG run ID
Inputs: dag_id (required), dag_run_id (required), state.
update_task_instanceC
Update a task instance by DAG ID, DAG run ID, and task ID
Inputs: dag_id (required), dag_run_id (required), task_id (required), state.
update_variableC
Update a variable by key
Inputs: key (required), value, description.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 68 tools
Most tools have distinct purposes targeting specific Airflow entities (DAGs, tasks, connections, variables, datasets), but there is some overlap between get_dag_details and get_dag, and between get_tasks and get_dag_tasks, which could cause minor confusion. The descriptions help differentiate, but the sheer number of tools increases cognitive load.
Tool names follow a highly consistent verb_noun pattern throughout (e.g., get_dag, create_connection, delete_variable, update_task_instance). There are no deviations in naming conventions, making the set predictable and easy to navigate.
With 68 tools, this server is overloaded for typical agent use. While Airflow is a complex system, this count far exceeds the 3-15 range for well-scoped servers, making it heavy and likely overwhelming for agents to handle efficiently.
The tool surface provides comprehensive CRUD/lifecycle coverage for all key Airflow domains (DAGs, tasks, connections, variables, datasets, pools, logs, XComs). There are no obvious gaps, and operations like create, get, update, delete, list, and state management are fully represented across entities.
Maintenance
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