MCP-Airflow-API

get_dataset_events

Fetch events for a specific Airflow dataset to monitor dataset updates and trigger DAG runs. Use limit and offset for pagination.

Instructions

[Tool Role]: Gets events for a specific dataset (v1 API only - v2 uses Assets).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_uri": "<dataset_uri>"
    },
    "name": "get_dataset_events"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`offset`	No
`dataset_uri`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.0.0Jul 31, 2026

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action and version scope, but does not mention whether this is a safe read operation, any authentication requirements, pagination behavior despite limit/offset parameters, or error conditions. This is a significant gap.

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

Conciseness5/5

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

The description is a single, front-loaded sentence with no filler. Every phrase contributes meaning, including the version constraint. It is appropriately concise for the limited information it provides.

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?

The tool has 3 parameters with 0% schema coverage and no annotations, so the description needs to supply more context. It covers purpose and version but omits parameter semantics and behavioral traits. The presence of an output schema reduces the need to explain return values, but other gaps remain, making it minimally viable at best (like the 'update_drive' calibration).

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 description must compensate for parameter understanding. It implies dataset_uri is the identifier ('a specific dataset'), but does not explain the role of limit and offset, their defaults, or how they interact. The description adds minimal value beyond the parameter names.

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

Purpose4/5

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

The description clearly identifies the action ('Gets events') and resource ('a specific dataset'), which is specific and distinct from sibling tools like get_dataset or list_dataset_events. The v1/v2 note adds scoping but does not explicitly contrast with sibling tool names, so it misses full sibling differentiation.

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

Usage Guidelines4/5

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

The description provides a clear usage context: 'v1 API only - v2 uses Assets.' This tells when to use (v1) and when not (v2, use Assets instead), serving as an exclusion and alternative. However, it does not name specific sibling tools or elaborate on general conditions for use, so it's not a full 5.

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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