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all_dag_event_summary

Get a summary of event logs across all DAGs in Apache Airflow to monitor workflow execution and identify issues.

Instructions

[Tool Role]: Provides summary of event logs across all DAGs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core handler function decorated with @mcp.tool(). Fetches the 1000 most recent event logs from Airflow API and computes a summary grouped by DAG ID and event type, returning counts per combination along with totals.
    async def all_dag_event_summary() -> Dict[str, Any]:
        """[Tool Role]: Provides summary of event logs across all DAGs."""
        resp = await airflow_request("GET", "/eventLogs?limit=1000")
        resp.raise_for_status()
        data = resp.json()
        
        event_summary = {}
        for event in data.get("event_logs", []):
            dag_id = event.get("dag_id", "unknown")
            event_type = event.get("event", "unknown")
            
            if dag_id not in event_summary:
                event_summary[dag_id] = {}
            if event_type not in event_summary[dag_id]:
                event_summary[dag_id][event_type] = 0
            event_summary[dag_id][event_type] += 1
        
        return {
            "event_summary": event_summary,
            "total_events": len(data.get("event_logs", [])),
            "unique_dags": len(event_summary)
        }
  • Calls register_common_tools(mcp) within v1_tools.py's register_tools function, registering the tool for Airflow API v1 (2.x). Preceded by setting the v1-specific airflow_request function.
    # Register all 56 common tools (includes management tools)
    common_tools.register_common_tools(mcp)
  • Calls register_common_tools(mcp) within v2_tools.py's register_tools function, registering the tool for Airflow API v2 (3.x+). Preceded by setting the v2-specific airflow_request function.
    # Register all 43 common tools
    common_tools.register_common_tools(mcp)
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool provides a 'summary', which implies a read-only, aggregated view, but doesn't specify what 'summary' entails (e.g., aggregated counts, recent events, or statistical data), whether it requires permissions, or if there are rate limits. The description adds minimal context beyond the basic purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's role. It's front-loaded with the key information and avoids redundancy. However, it could be slightly more structured by including usage context, but given its brevity and clarity, it earns a high score for conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 0 parameters, 100% schema coverage, and an output schema exists (which likely defines the summary structure), the description is adequate for a simple read operation. However, it lacks details on behavioral aspects like permissions or summary scope, which are important for a tool interacting with event logs. It's minimally viable but could be more complete by addressing these gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter details, so it appropriately focuses on the tool's purpose. A baseline of 4 is applied as it compensates for the lack of parameters by clearly stating what the tool does without unnecessary parameter information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Provides summary of event logs across all DAGs', which gives a clear verb ('Provides summary') and resource ('event logs across all DAGs'). However, it doesn't differentiate from sibling tools like 'get_event_log' or 'list_event_logs', which likely provide more detailed or filtered event log data. The purpose is understandable but lacks sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description offers no guidance on when to use this tool versus alternatives. It doesn't mention when this summary view is preferred over more detailed event log tools (e.g., 'get_event_log' or 'list_event_logs'), nor does it specify any prerequisites or exclusions. Usage is implied by the name but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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