Airflow Pool Management and Utilization Monitoring with mcp-airflow-api

Pool management in mcp-airflow-api is handled by two asynchronous tools—list_pools and get_pool—that expose Airflow's REST API to query slot allocation and real-time utilization metrics.

The call518/mcp-airflow-api repository provides a Model Context Protocol (MCP) server that exposes Apache Airflow's administrative capabilities through structured tools. For operators needing to track resource constraints and prevent task starvation, understanding Airflow pool management and utilization monitoring is essential to maintaining healthy data pipelines.

Core Pool Management Tools

The pool management capabilities are implemented in src/mcp_airflow_api/tools/common_tools.py (lines 675-688), which defines two high-level asynchronous tools that wrap Airflow's REST API endpoints.

Listing All Pools with Pagination

The list_pools tool retrieves a paginated overview of every configured Airflow pool. It accepts limit (default 20) and offset (default 0) parameters to handle large deployments efficiently.

This tool calls GET /pools via the internal airflow_request helper defined in src/mcp_airflow_api/functions.py. The response includes each pool's name, total slots, and current used_slots, enabling immediate visibility into resource allocation across the cluster.

Retrieving Specific Pool Details

For targeted inspection, the get_pool tool accepts a pool_name parameter and calls GET /pools/{pool_name}. This returns the complete pool definition including the optional description field, total slot capacity, and real-time utilization metrics.

Monitoring Pool Utilization

Effective monitoring relies on comparing the used_slots value against the total slots allocated to each pool. The mcp-airflow-api tools expose these metrics as integers, allowing operators to calculate utilization percentages and identify bottlenecks before task starvation occurs.

Calculating Utilization Percentages

When processing the JSON response from either pool tool, calculate utilization as follows:

utilization_percent = (used_slots / slots) * 100

A pool exceeding 80% utilization typically indicates approaching capacity constraints, while 100% used_slots with pending tasks signals immediate resource exhaustion.

Integration with Troubleshooting Workflows

The repository includes built-in troubleshooting logic in src/mcp_airflow_api/mcp_main.py (lines 135-138). When the system detects "Resource Issues" flags during pipeline failures, it explicitly suggests invoking list_pools to check current utilization metrics. This integration ensures pool monitoring becomes a standard diagnostic step in incident response procedures.

Implementation Examples

The following examples demonstrate practical usage patterns for the pool management tools.

Basic Pool Query

Retrieve all pools with increased pagination to ensure complete cluster visibility:

# Using the MCP tool interface

result = await mcp.run_tool("list_pools", limit=100)
print(result)

# Output: {"pools": [{"name": "default", "slots": 128, "used_slots": 42, ...}, ...]}

Inspect a specific high-priority pool:

pool_details = await mcp.run_tool("get_pool", pool_name="critical_workloads")
print(f"Pool capacity: {pool_details['slots']}")
print(f"Currently used: {pool_details['used_slots']}")

Automated Monitoring Script

Implement periodic utilization checks with alerting thresholds:

import asyncio
from mcp_airflow_api import create_mcp_server

async def monitor_pools():
    mcp = create_mcp_server()
    while True:
        pools = await mcp.run_tool("list_pools")
        for p in pools["pools"]:
            utilization = (p["used_slots"] / p["slots"]) * 100
            if utilization > 80:
                print(f"⚠️ High utilization: {p['name']} @ {utilization:.1f}%")
        await asyncio.sleep(300)  # check every 5 minutes

asyncio.run(monitor_pools())

Key Source Files

Understanding the implementation requires familiarity with these specific files in the call518/mcp-airflow-api repository:

• src/mcp_airflow_api/tools/common_tools.py (lines 675-688): Defines the list_pools and get_pool tools that wrap the Airflow REST API.

• src/mcp_airflow_api/mcp_main.py (lines 135-138): Contains the troubleshooting workflow logic that references pool utilization checks during resource issue diagnosis.

• src/mcp_airflow_api/functions.py: Implements the airflow_request helper function used by pool tools to execute HTTP requests against the Airflow REST API.

• src/mcp_airflow_api/__main__.py: Entry point that initializes the MCP server and registers the pool management tools.

Summary

• The call518/mcp-airflow-api exposes pool management through two primary asynchronous tools: list_pools for paginated overviews and get_pool for detailed inspection.

• Both tools query Airflow's REST API (GET /pools and GET /pools/{name}) and return real-time metrics including slots (capacity) and used_slots (current utilization).

• Monitoring utilization requires calculating the ratio of used_slots to slots, with the troubleshooting workflow in mcp_main.py explicitly recommending these checks when resource issues occur.

• Implementation supports both interactive MCP prompts and automated monitoring scripts that can trigger alerts when utilization exceeds defined thresholds.

Frequently Asked Questions

How do I check which Airflow pools are nearing capacity?

Use the list_pools tool to retrieve all pools, then calculate the utilization percentage by dividing used_slots by slots for each entry. Pools exceeding 80% utilization typically indicate approaching capacity constraints that require attention.

What is the difference between list_pools and get_pool?

The list_pools tool provides a paginated overview of all configured pools with basic metrics (name, slots, used_slots), while get_pool retrieves detailed information for a specific pool including the optional description field and precise utilization counts.

Where does the mcp-airflow-api get its pool utilization data?

The tools call Airflow's native REST API endpoints (GET /pools and GET /pools/{pool_name}) via the airflow_request helper function defined in src/mcp_airflow_api/functions.py, ensuring real-time accuracy with the Airflow metadata database.

Can I automate alerts when pool utilization exceeds thresholds?

Yes, by creating a client script that periodically invokes list_pools and calculates utilization ratios. The repository's troubleshooting workflow in src/mcp_airflow_api/mcp_main.py (lines 135-138) demonstrates this pattern, suggesting pool checks when resource issues are detected.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}