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astronomer

astro-airflow-mcp

Official
by astronomer

list_assets

Retrieve all tracked data assets and datasets in Airflow, including their URIs, IDs, creation and update times, and relationships with consuming DAGs and producing tasks. Ideal for inspecting data lineage and dependencies.

Instructions

Get data assets and datasets tracked by Airflow (data lineage).

Use this tool when the user asks about:

  • "What datasets exist?" or "List all assets"

  • "What data does this DAG produce/consume?"

  • "Show me data dependencies" or "What's the data lineage?"

  • "Which DAGs use dataset X?"

  • Data freshness or update events

Assets represent datasets or files that DAGs produce or consume. This enables data-driven scheduling where DAGs wait for data availability.

Returns asset information including:

  • uri: Unique identifier for the asset (e.g., s3://bucket/path)

  • id: Internal asset ID

  • created_at: When this asset was first registered

  • updated_at: When this asset was last updated

  • consuming_dags: Which DAGs depend on this asset

  • producing_tasks: Which tasks create/update this asset

Returns: JSON with list of all assets and their producing/consuming relationships

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Despite no annotations, the description clearly indicates this is a read-only operation with no side effects, and details the returned fields (uri, id, consuming_dags, etc.). It provides sufficient behavioral context beyond the input schema.

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 well-structured with clear sections and bullet points for return fields. It is moderately concise but includes necessary context; no wasted sentences.

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

Completeness5/5

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

Given zero parameters, the description fully explains the tool's purpose and output. The presence of an output schema is complemented by a detailed textual description of return fields, making it complete for this simple tool.

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?

With zero parameters and 100% schema coverage, the description adds meaning by detailing the return structure. Since there are no parameters to document, the baseline 3 is exceeded by the clear output specification.

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 retrieves data assets and datasets tracked by Airflow, providing specific example queries. It distinguishes from related tools like list_asset_events and get_upstream_asset_events by focusing on assets themselves.

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 explicitly lists example user queries to guide usage, such as 'What datasets exist?' and 'Show me data dependencies'. While it does not explicitly exclude alternative tools, the examples cover primary use cases effectively.

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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