Retrieve and filter Apache Airflow event logs to monitor DAG runs, task executions, and system events for debugging and auditing purposes.
List log entries from event log
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| order_by | No | ||
| dag_id | No | ||
| task_id | No | ||
| run_id | No | ||
| map_index | No | ||
| try_number | No | ||
| event | No | ||
| owner | No | ||
| before | No | ||
| after | No | ||
| included_events | No | ||
| excluded_events | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0Input schema / properties / after / titleRemoved value: -"After"Input schema / properties / before / titleRemoved value: -"Before"Input schema / properties / dag_id / titleRemoved value: -"Dag Id"Input schema / properties / event / titleRemoved value: -"Event"Input schema / properties / excluded_events / titleRemoved value: -"Excluded Events"Input schema / properties / included_events / titleRemoved value: -"Included Events"Input schema / properties / limit / titleRemoved value: -"Limit"Input schema / properties / map_index / titleRemoved value: -"Map Index"Input schema / properties / offset / titleRemoved value: -"Offset"Input schema / properties / order_by / titleRemoved value: -"Order By"Input schema / properties / owner / titleRemoved value: -"Owner"Input schema / properties / run_id / titleRemoved value: -"Run Id"Input schema / properties / task_id / titleRemoved value: -"Task Id"Input schema / properties / try_number / titleRemoved value: -"Try Number"Input schema / titleRemoved value: -"get_event_logsArguments"Output schema / (root)Previous value: -{
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-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It only states the action ('List log entries') without any information about permissions, rate limits, pagination (implied by limit/offset but not explained), response format, or side effects. For a tool with 14 parameters and no annotations, this is a critical gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words: 'List log entries from event log.' It is front-loaded and appropriately sized for its minimal content, though this conciseness comes at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (14 parameters, no schema descriptions, no annotations, no output schema), the description is severely incomplete. It lacks essential context such as what the event log contains, how to filter or paginate, what the return structure is, and how it differs from similar tools. For a data retrieval tool with many options, this is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 14 parameters are documented in the schema. The description adds no information about any parameters—it doesn't mention filtering by dag_id, task_id, date ranges (before/after), ordering, or pagination. With high parameter count and zero coverage, the description fails to compensate, leaving semantics entirely unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List log entries from event log' states the basic action (list) and resource (log entries from event log), which is clear but vague. It doesn't specify what kind of event log (e.g., Airflow DAG events) or differentiate from sibling tools like get_event_log (singular) or get_log (general logs). The purpose is understandable but lacks specificity and sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like get_event_log (singular), get_log (general logs), and get_dataset_events (dataset-specific), there's no indication of context, prerequisites, or exclusions. The agent must infer usage from the tool name alone, which is insufficient.
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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