MCP-Airflow-API

get_dataset

Retrieve the full details of an Airflow dataset using its dataset URI, providing metadata and configuration for effective workflow monitoring and management.

Instructions

[Tool Role]: Gets details of 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"
  }
}

Input Schema

Name	Required	Description	Default
`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

A3.9/5.0

Behavior3/5

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 adds useful context by disclosing the v1/v2 difference and that v2 uses Assets. However, it does not describe the return format, potential errors, or authentication needs, though the output schema may cover return shape.

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 concise sentence that front-loads the core purpose and adds a version qualifier. There is no filler or redundant information, making it highly efficient.

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

Completeness4/5

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

For a simple one-parameter read tool with an output schema, the description covers the essential purpose and an important version distinction. It could explicitly differentiate from list_datasets or note read-only behavior, but the low complexity and output schema make this sufficient.

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%, and the description does not elaborate on dataset_uri (e.g., format, examples, required semantics). The parameter name is self-explanatory to a degree, but the description fails to compensate for the lack of schema documentation, adding no meaning beyond the schema field.

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

Purpose5/5

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

The description clearly states the tool gets details of a specific dataset, with a specific verb ('gets details') and resource ('dataset'). It also distinguishes from sibling tools by noting this is v1 API only and v2 uses Assets, which helps differentiate it from other dataset-related tools.

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 context for when to use this tool by noting it is v1-specific and that v2 uses Assets. However, it does not explicitly name alternatives like list_datasets or provide exclusions beyond the version caveat, so it's clear but not exhaustive.

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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