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yangkyeongmo

MCP Server for OpenMetadata

by yangkyeongmo

create_ml_model

Create a new machine learning model in OpenMetadata to track and manage ML assets within your data ecosystem.

Instructions

Create a new ML model in OpenMetadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_dataYes
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 states 'Create' implying a write operation but doesn't disclose behavioral traits like required permissions, whether it's idempotent, error handling, or rate limits. This is inadequate for a mutation tool with zero annotation coverage.

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, efficient sentence with no wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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?

Given the tool's complexity (mutation with nested object parameter), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't address key aspects like parameter details, behavioral context, or return values, leaving significant gaps.

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 provides no information about the single parameter 'model_data'. It doesn't explain what 'model_data' should contain, its structure, or examples, failing to compensate for the lack of schema documentation.

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 states the action ('Create') and resource ('new ML model in OpenMetadata'), providing specific verb+resource. However, it doesn't differentiate from sibling tools like 'create_basic_test_suite' or 'create_chart' beyond the ML model focus, missing explicit distinction.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives is provided. The description lacks context about prerequisites, timing, or comparisons to other creation tools in the sibling list, leaving usage unclear.

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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