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fabdendev

dagster-mcp

by fabdendev

Get Asset Details

get_asset_details

Retrieve metadata for Dagster assets, including lineage, partitions, and dependencies. Use it to understand what an asset depends on, what depends on it, or whether it is partitioned.

Instructions

Get detailed metadata for one or more assets: description, lineage, and partitions.

  • asset_keys: list of asset name strings (e.g. ['my_extract', 'my_load'])

Returns per asset: assetKey, description, groupName, op name, isObservable, isPartitioned, partitionDefinition, dependencyKeys (upstream assets), dependedByKeys (downstream assets), and the latest materialization (runId + timestamp).

When to use: to understand an asset's lineage (what it depends on and what depends on it), check if it's partitioned, or get its description. Use search_assets first if you don't know the exact key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
envNo
asset_keysYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.0

TDQS

A4.1/5.0
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 implies a read ('Get') and shows it accepts multiple keys in one call, but it does not state read-only semantics explicitly, behavior on missing/invalid keys, or any auth/rate-limit characteristics. An output schema already exists, so the extensive return-field listing adds little behavioral disclosure beyond structured data.

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?

Front-loaded with the core purpose, then a parameter bullet, then a 'When to use' block — a clean structure. The long enumeration of return fields is partially redundant given an output schema exists, which costs a little efficiency.

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?

Because an output schema exists, the return-value explanation is not strictly needed, but usage and the primary parameter are well covered. The only real gap is the undocumented env parameter, which is minor for a read tool of this shape.

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

Parameters3/5

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. It documents asset_keys well, including type and a concrete example (['my_extract', 'my_load']), but the second parameter, env, is never mentioned, leaving half the parameters undocumented.

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?

States a specific verb and resource ('Get detailed metadata for one or more assets') and enumerates what that metadata includes: description, lineage, and partitions. It also distinguishes itself from the sibling search_assets by telling the agent to use that tool first when the key is unknown.

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

Usage Guidelines5/5

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

The 'When to use' block gives concrete conditions (understand lineage, check partitioning, get description) and names the alternative (search_assets) with the exact trigger for preferring it. Nothing about tool selection is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.