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list_event_logs

Retrieve Airflow event logs to monitor DAG executions and task performance. Filter by DAG ID and paginate results for focused troubleshooting.

Instructions

[Tool Role]: Lists event logs from Airflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idNo
limitNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The core handler function implementing the list_event_logs tool. Queries the Airflow eventLogs API endpoint with optional dag_id filter and pagination (limit/offset). Returns JSON response from the API.
    async def list_event_logs(dag_id: Optional[str] = None, limit: int = 20, offset: int = 0) -> Dict[str, Any]:
        """[Tool Role]: Lists event logs from Airflow."""
        params = {'limit': limit, 'offset': offset}
        if dag_id:
            params['dag_id'] = dag_id
        
        query_string = "&".join([f"{k}={v}" for k, v in params.items()])
        resp = await airflow_request("GET", f"/eventLogs?{query_string}")
        resp.raise_for_status()
        return resp.json()
  • Registration entry point for v1 API tools. Sets v1-specific airflow_request function and calls register_common_tools(mcp), which registers list_event_logs among other common tools.
    def register_tools(mcp):
        """Register v1 tools by importing common tools with v1 request function."""
        
        logger.info("Initializing MCP server for Airflow API v1")
        logger.info("Loading Airflow API v1 tools (Airflow 2.x)")
        
        # Set the global request function to v1
        common_tools.airflow_request = airflow_request_v1
        
        # Register all 56 common tools (includes management tools)
        common_tools.register_common_tools(mcp)
        
        # V1 has no exclusive tools - all tools are shared with v2
        
        logger.info("Registered all Airflow API v1 tools (56 tools: 43 core + 13 management tools)")
  • Registration entry point for v2 API tools. Sets v2-specific airflow_request function and calls register_common_tools(mcp), which registers list_event_logs among other common tools.
    def register_tools(mcp):
        """Register v2 tools: common tools + v2-exclusive asset tools."""
        
        logger.info("Initializing MCP server for Airflow API v2")
        logger.info("Loading Airflow API v2 tools (Airflow 3.0+)")
        
        # Set the global request function to v2
        common_tools.airflow_request = airflow_request_v2
        
        # 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. It states the tool lists event logs, implying a read-only operation, but fails to disclose behavioral traits such as pagination behavior (hinted by limit/offset parameters), authentication needs, rate limits, or what constitutes an 'event log' in Airflow. This leaves significant gaps in understanding how the tool behaves beyond basic listing.

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 concise with a single sentence, front-loaded with the tool's role. It avoids unnecessary words, but the brevity comes at the cost of completeness, as it omits critical details needed for effective tool use. The structure is clear but under-specified.

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

Completeness2/5

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

Given the tool has 3 parameters with 0% schema coverage, no annotations, and an output schema (which might help with return values), the description is incomplete. It does not explain parameter usage, behavioral context, or differentiation from siblings, making it inadequate for a tool with multiple inputs and potential complexity in Airflow's event logging system.

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

Parameters2/5

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

Schema description coverage is 0%, so the schema provides no parameter descriptions. The tool description does not mention any parameters or their semantics (e.g., dag_id for filtering by DAG, limit/offset for pagination). With 3 parameters undocumented, the description fails to compensate for the lack of schema details, offering no value beyond the bare schema.

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 'Lists event logs from Airflow', which clearly indicates a read/list operation on event logs. However, it lacks specificity about what event logs contain (e.g., DAG runs, task instances) and does not differentiate from siblings like 'get_event_log' or 'list_dataset_events', making it vague in distinguishing its exact scope.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention related tools like 'get_event_log' (which might fetch a single log) or 'list_dataset_events' (which might list different types of events), nor does it specify prerequisites or contexts for usage, leaving the agent without direction on tool selection.

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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