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zenml-io

ZenML MCP Server

Official
by zenml-io

zenml_get_resource

Retrieve a specific allowlisted ZenML resource by its ID, including artifact versions, model versions, run steps, or stack components, with parent identifiers as needed.

Instructions

Get one allowlisted ZenML resource by its identifier.

Artifact versions, model versions, and run steps require their parent identifier. Stack components require their fixed component type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hydrateNo
model_idNo
project_idNo
artifact_idNo
resource_idYes
resource_typeYes
component_typeNo
pipeline_run_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.0

TDQS

B3/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It doesn't mention any side effects, access requirements, or rate limits. It doesn't state whether the tool is read-only, but 'Get' implies read-only; however, without annotations, this is implicit and not explicitly disclosed. Could mention that it only retrieves allowlisted resources.

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 concise, with two sentences. It front-loads the core purpose and then adds necessary context about parent identifiers and component type. No fluff, each sentence earns its place.

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

Completeness3/5

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

Given the complexity of 8 parameters with 0% schema coverage and no annotations, the description is insufficient. It doesn't specify which parameters are required for each resource type, nor does it explain the output format (though output schema exists, so that's covered). It leaves ambiguity for agents on how to fill parameters for different resource types.

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. The description explains that parent identifiers are needed for certain resource types and component type for stack components, which adds meaning to those parameters. However, it doesn't explain hydrate, project_id, model_id, artifact_id, pipeline_run_id in detail, leaving uncertainty about which to use for which resource type.

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 states the tool gets a single allowlisted ZenML resource by identifier, which is clear. It distinguishes from siblings like list and describe by emphasizing 'one' resource. However, it doesn't explicitly name siblings like zenml_describe_resources to differentiate, but the purpose is clear enough.

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

Usage Guidelines3/5

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

The description provides some usage context by noting that artifact versions, model versions, and run steps require parent identifiers, and stack components require component type. This implies when to use those parameters but doesn't explicitly say when to use this tool over zenml_describe_resources or list. No exclusions or alternatives mentioned.

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