mcp-airflow-api 1.0.6

Model Context Protocol (MCP) server for Apache Airflow API integration. Provides comprehensive tools for managing Airflow clusters including service operations, configuration management, status monitoring, and request tracking.

---

Model Context Protocol (MCP) server for Apache Airflow API integration.
This project provides natural language MCP tools for essential Airflow cluster operations.

Note: To minimize operational risk, this MCP server currently focuses on read-only (query) operations only. APIs that modify the target Airflow cluster (e.g., triggering or pausing DAGs) are planned but currently on hold.

---

MCP-Airflow-API

Tested and supported Airflow version: 2.10.2 (API Version: v1) and WSL(networkingMode = bridged)

• Airflow API Documents

Example Query - List DAGs

Usages

This MCP server supports two connection modes: stdio (traditional) and streamable-http (Docker-based). The transport mode is automatically determined by the MCP_SERVER_PORT environment variable.

Method 1: Traditional stdio Mode (Local Installation)

{
  "mcpServers": {
    "airflow-api": {
      "command": "uvx",
      "args": ["--python", "3.11", "mcp-airflow-api"],
      "env": {
        "AIRFLOW_API_URL": "http://localhost:8080/api/v1",
        "AIRFLOW_API_USERNAME": "airflow",
        "AIRFLOW_API_PASSWORD": "airflow",
        "AIRFLOW_LOG_LEVEL": "INFO"
      }
    }
  }
}

Method 2: Docker streamable-http Mode

{
  "mcpServers": {
    "airflow-api": {
      "type": "streamable-http",
      "url": "http://host.docker.internal:18002/mcp"
    }
  }
}

Transport Selection Logic:

• stdio mode: When MCP_SERVER_PORT environment variable is NOT set
• streamable-http mode: When MCP_SERVER_PORT environment variable is set

---

QuickStart (Demo - streamable-http): Running OpenWebUI and MCP-Airflow-API with Docker

1. Prepare an Airflow Demo cluster

   • Try this: Airflow-Docker-Compose
   • (Optional) See Official Airflow Docker Install Guide

2. Install Docker and Docker Compose

   • Ensure Docker Engine and Docker Compose are installed and running

Setup and Configuration

3. Clone and Configure

   git clone <repository-url>
   cd MCP-Airflow-API
   

4. Ensure mcp-config.json

   • Check and edit mcp-config.json.streamable-http
   • The file is pre-configured for streamable-http transport

5. Ensure docker-compose.yml

   • Check Network Port numbers that you want.
   • (NOTE) This Tested on WSL2(networkingMode = bridged)

6. Start the Docker Services

   docker-compose up -d
   

Service Access and Verification

6. Check MCP Server REST-API (via MCPO Swagger)

   • Access: http://localhost:8002/docs
   • Verify all Airflow API endpoints are available

7. Access Open WebUI

   • URL: http://localhost:3002
   • The interface includes integrated MCPO proxy support

8. Register the MCP server

   • In [Settings] — [Tools], add the API address of the “airflow-api” tool (the link displayed in the MCPO Swagger), e.g., http://localhost:8001/airflow-api

9. Setup LLM

   • In [Admin Pannel] - [Setting] - [Connection], configure API Key for OpenAI or Ollama.

10. Completed!

---

Docker Configuration

The project includes a comprehensive Docker Compose setup with three separate services for optimal isolation and management:

Services Architecture

1. open-webui: Web interface (port 3002)

   • Custom Open WebUI with integrated MCPO proxy support
   • Built from Dockerfile.OpenWebUI-MCPO-Proxy

2. mcp-server: MCP Airflow API server (port 18002, internal 18000)

   • FastMCP-based MCP server with Airflow API tools
   • Built from Dockerfile.MCP-Server (Rocky Linux 9.3, Python 3.11)
   • Runs streamable-http transport when MCP_SERVER_PORT is set

3. mcpo-proxy: MCP-to-OpenAPI proxy (port 8002)

   • MCPO proxy for converting MCP tools to REST API endpoints
   • Built from Dockerfile.MCPO-Proxy (Rocky Linux 9.3, Python 3.11)
   • Provides Swagger documentation at /docs

Configuration Files

The Docker setup uses these configuration files:

• docker-compose.yml: Multi-service orchestration
• mcp-config.json.stdio: MCPO proxy configuration for stdio transport
• mcp-config.json.streamable-http: MCPO proxy configuration for streamable-http transport
• Dockerfile.MCPO-Proxy: MCPO proxy container with Rocky Linux 9.3 base
• Dockerfile.MCP-Server: MCP server container with FastMCP runtime

Environment Variables

The MCP server container uses these environment variables:

• MCP_SERVER_PORT=18000: Enables streamable-http transport mode
• AIRFLOW_API_URL: Your Airflow API endpoint
• AIRFLOW_API_USERNAME: Airflow username
• AIRFLOW_API_PASSWORD: Airflow password

Service Access

• Open WebUI: http://localhost:3002
• MCP Server: http://localhost:18002
• MCPO Proxy: http://localhost:8002

Container-to-Host Communication

The configuration uses host.docker.internal:18002 for proper Docker networking when connecting from containers to host services.

Features

• List all DAGs in the Airflow cluster
• Monitor running/failed DAG runs
• Trigger DAG runs on demand
• Check cluster health and version information
• Minimal, LLM-friendly output for all tools
• Easy integration with MCP Inspector, OpenWebUI, Smithery, etc.
• Enhanced for Large-Scale Environments: Improved default limits and pagination support for enterprise Airflow deployments (100+ to 1000+ DAGs)

Environment Variables Configuration

Required Environment Variables

These environment variables are essential for connecting to your Airflow instance:

• AIRFLOW_API_URL: The base URL of your Airflow REST API endpoint

  • Example: http://localhost:8080/api/v1
  • Example: https://airflow.company.com/api/v1

• AIRFLOW_API_USERNAME: Username for Airflow API authentication

  • Example: airflow
  • Example: admin

• AIRFLOW_API_PASSWORD: Password for Airflow API authentication

  • Example: airflow
  • Example: your-secure-password

Transport Control Variables

• MCP_SERVER_PORT: Controls the transport mode selection
  • When NOT set: Uses stdio transport (traditional MCP mode)
  • When set: Uses streamable-http transport (Docker mode)
  • Example: 18000 (for Docker container internal port)

Optional Configuration Variables

• AIRFLOW_LOG_LEVEL: Controls logging verbosity
  • Values: DEBUG, INFO, WARNING, ERROR
  • Default: INFO

---

Available MCP Tools

DAG Management

• list_dags(limit=20, offset=0, fetch_all=False, id_contains=None, name_contains=None)
  Returns all DAGs registered in the Airflow cluster with pagination support.
  Output: dag_id, dag_display_name, is_active, is_paused, owners, tags, plus pagination info (total_entries, limit, offset, has_more_pages, next_offset, pagination_info)

  Pagination Examples:

  • First 20 DAGs: list_dags()

  • Next 20 DAGs: list_dags(limit=20, offset=20)

  • Large batch: list_dags(limit=100, offset=0)

  • All DAGs at once: list_dags(limit=1000)

  • id_contains="etl" → Only DAGs whose dag_id contains "etl"

  • name_contains="daily" → Only DAGs whose display_name contains "daily"

  • If both are specified, only DAGs matching both conditions are returned

• running_dags
  Returns all currently running DAG runs.
  Output: dag_id, run_id, state, execution_date, start_date, end_date

• failed_dags
  Returns all recently failed DAG runs.
  Output: dag_id, run_id, state, execution_date, start_date, end_date

• trigger_dag(dag_id)
  Immediately triggers the specified DAG.
  Output: dag_id, run_id, state, execution_date, start_date, end_date

• pause_dag(dag_id)
  Pauses the specified DAG (prevents scheduling new runs).
  Output: dag_id, is_paused

• unpause_dag(dag_id)
  Unpauses the specified DAG (allows scheduling new runs).
  Output: dag_id, is_paused

Cluster Management & Health

• get_health
  Get the health status of the Airflow webserver instance.
  Output: metadatabase, scheduler, overall health status

• get_version
  Get version information of the Airflow instance.
  Output: version, git_version, build_date, api_version

Pool Management

• list_pools(limit=20, offset=0)
  List all pools in the Airflow instance with pagination support.
  Output: pools, total_entries, limit, offset, pool details with slots usage

• get_pool(pool_name)
  Get detailed information about a specific pool.
  Output: name, slots, occupied_slots, running_slots, queued_slots, open_slots, description, utilization_percentage

Variable Management

• list_variables(limit=20, offset=0, order_by="key")
  List all variables stored in Airflow with pagination support.
  Output: variables, total_entries, limit, offset, variable details with keys, values, and descriptions

• get_variable(variable_key)
  Get detailed information about a specific variable by its key.
  Output: key, value, description, is_encrypted

Task Instance Management

• list_task_instances_all(dag_id=None, dag_run_id=None, execution_date_gte=None, execution_date_lte=None, start_date_gte=None, start_date_lte=None, end_date_gte=None, end_date_lte=None, duration_gte=None, duration_lte=None, state=None, pool=None, queue=None, limit=20, offset=0)
  Lists task instances across all DAGs or filtered by specific criteria with comprehensive filtering options.
  Output: task_instances, total_entries, limit, offset, applied_filters

• get_task_instance_details(dag_id, dag_run_id, task_id)
  Retrieves detailed information about a specific task instance.
  Output: Comprehensive task instance details including execution info, state, timing, configuration, and metadata

• list_task_instances_batch(dag_ids=None, dag_run_ids=None, task_ids=None, execution_date_gte=None, execution_date_lte=None, start_date_gte=None, start_date_lte=None, end_date_gte=None, end_date_lte=None, duration_gte=None, duration_lte=None, state=None, pool=None, queue=None)
  Lists task instances in batch with multiple filtering criteria for bulk operations.
  Output: task_instances, total_entries, applied_filters, batch processing results

• get_task_instance_extra_links(dag_id, dag_run_id, task_id)
  Lists extra links for a specific task instance (e.g., monitoring dashboards, logs, external resources).
  Output: task_id, dag_id, dag_run_id, extra_links, total_links

• get_task_instance_logs(dag_id, dag_run_id, task_id, try_number=1, full_content=False, token=None)
  Retrieves logs for a specific task instance and its try number with content and metadata.
  Output: task_id, dag_id, dag_run_id, try_number, content, continuation_token, metadata

XCom Management

• list_xcom_entries(dag_id, dag_run_id, task_id, limit=20, offset=0)
  Lists XCom entries for a specific task instance.
  Output: dag_id, dag_run_id, task_id, xcom_entries, total_entries, limit, offset

• get_xcom_entry(dag_id, dag_run_id, task_id, xcom_key, map_index=-1)
  Retrieves a specific XCom entry for a task instance.
  Output: dag_id, dag_run_id, task_id, xcom_key, map_index, key, value, timestamp, execution_date, run_id

DAG Analysis & Monitoring

• get_dag(dag_id)
  Retrieves comprehensive details for a specific DAG.
  Output: dag_id, description, schedule_interval, owners, tags, start_date, next_dagrun, etc.

• dag_graph(dag_id)
  Retrieves task dependency graph structure for a specific DAG.
  Output: dag_id, tasks, dependencies, task relationships

• list_tasks(dag_id)
  Lists all tasks for a specific DAG.
  Output: dag_id, tasks, task configuration details
  Output: dag_id, tasks, dependencies, task relationships

• dag_code(dag_id)
  Retrieves the source code for a specific DAG.
  Output: dag_id, file_token, source_code

• list_event_logs(dag_id=None, task_id=None, run_id=None, limit=20, offset=0)
  Lists event log entries with optional filtering.
  Optimized limit: Default is 20 for better performance while maintaining good coverage.
  Output: event_logs, total_entries, limit, offset, has_more_pages, next_offset, pagination_info

• get_event_log(event_log_id)
  Retrieves a specific event log entry by ID.
  Output: event_log_id, when, event, dag_id, task_id, run_id, etc.

• all_dag_event_summary()
  Retrieves event count summary for all DAGs.
  Improved limit: Uses limit=1000 for DAG retrieval to avoid missing DAGs in large environments.
  Output: dag_summaries, total_dags, total_events

• list_import_errors(limit=20, offset=0)
  Lists import errors with optional filtering.
  Optimized limit: Default is 20 for better performance while maintaining good coverage.
  Output: import_errors, total_entries, limit, offset, has_more_pages, next_offset, pagination_info

• get_import_error(import_error_id)
  Retrieves a specific import error by ID.
  Output: import_error_id, filename, stacktrace, timestamp

• all_dag_import_summary()
  Retrieves import error summary for all DAGs.
  Output: import_summaries, total_errors, affected_files

• dag_run_duration(dag_id, limit=50)
  Retrieves run duration statistics for a specific DAG.
  Improved limit: Default increased from 10 to 50 for better statistical analysis.
  Output: dag_id, runs, duration analysis, success/failure stats

• dag_task_duration(dag_id, run_id=None)
  Retrieves task duration information for a specific DAG run.
  Output: dag_id, run_id, tasks, individual task performance

• dag_calendar(dag_id, start_date=None, end_date=None, limit=20)
  Retrieves calendar/schedule information for a specific DAG.
  Configurable limit: Default is 20, can be increased up to 1000 for bulk analysis.
  Output: dag_id, schedule_interval, runs, upcoming executions

---

Example Queries

Go to Example Queries

---

Prompt Template

The package exposes a tool get_prompt_template that returns either the entire template, a specific section, or just the headings. Three MCP prompts (prompt_template_full, prompt_template_headings, prompt_template_section) are also registered for discovery.

MCP Prompts

For easier discoverability in MCP clients (so prompts/list is not empty), the server now registers three prompts:

• prompt_template_full – returns the full canonical template
• prompt_template_headings – returns only the section headings
• prompt_template_section – takes a section argument (number or keyword) and returns that section

You can still use the get_prompt_template tool for programmatic access or when you prefer tool invocation over prompt retrieval.

Single canonical English prompt template guides safe and efficient tool selection.

Files: • Packaged: src/mcp_airflow_api/prompt_template.md (distributed with PyPI)
• (Optional workspace root copy PROMPT_TEMPLATE.md may exist for editing; packaged copy is the one loaded at runtime.)

Retrieve dynamically via MCP tool: • get_prompt_template() – full template
• get_prompt_template("tool map") – only the tool mapping section
• get_prompt_template("3") – section 3 (tool map)
• get_prompt_template(mode="headings") – list all section headings

Policy: Only English is stored; LLM는 사용자 질의 언어와 무관하게 영어 지침을 내부 추론용으로 사용하고, 사용자 응답은 필요 시 다국어로 생성한다.

---

Main Tool Files

• MCP tool definitions: src/mcp_airflow_api/airflow_api.py
• Utility functions: src/mcp_airflow_api/functions.py

---

Pagination Guide for Large Airflow Environments

Understanding DAG Pagination

The list_dags() function now supports pagination to handle large Airflow environments efficiently:

Default Behavior:

• Returns first 100 DAGs by default
• Includes pagination metadata in response

Pagination Response Structure:

{
  "dags": [...],
  "total_entries": 1500,
  "limit": 100,
  "offset": 0,
  "returned_count": 100,
  "has_more_pages": true,
  "next_offset": 100,
  "pagination_info": {
    "current_page": 1,
    "total_pages": 15,
    "remaining_count": 1400
  }
}

Pagination Strategies

🔍 Exploratory (Recommended for LLMs):

1. list_dags() → Check first 20 DAGs
2. Use has_more_pages to determine if more exist
3. list_dags(limit=20, offset=20) → Get next 20
4. Continue as needed

📊 Complete Analysis:

→ Automatically fetches ALL DAGs regardless of count

⚡ Quick Large Queries:

list_dags(limit=500)
→ Get up to 500 DAGs in one call

Best Practices

• Small Airflow (< 50 DAGs): Use default list_dags()
• Medium Airflow (50-500 DAGs): Use list_dags(limit=100) or list_dags(limit=200)
• Memory-conscious: Use default limits (20) with manual pagination

---

Logging & Observability

• Structured logs for all tool invocations and HTTP requests
• Control log level via environment variable (AIRFLOW_LOG_LEVEL) or CLI flag (--log-level)
• Supported levels: DEBUG, INFO, WARNING, ERROR, CRITICAL

---

Roadmap

This project starts with a minimal set of essential Airflow management tools. Many more useful features and tools for Airflow cluster operations will be added soon, including advanced monitoring, DAG/task analytics, scheduling controls, and more. Contributions and suggestions are welcome!

---

Additional Links

• Code

---

Testing

This project includes comprehensive tests for the prompt template functionality.

Running Tests

# Install development dependencies
uv sync

# Run all tests
uv run pytest

# Run tests with verbose output
uv run pytest -v

# Run specific test file
uv run pytest tests/test_prompt_template.py -v

---

More ScreenShoots

---

License

This project is licensed under the MIT License.