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yangkyeongmo

MCP Server for OpenMetadata

by yangkyeongmo

delete_ml_model

Remove machine learning models from OpenMetadata to manage model lifecycle and maintain data governance standards.

Instructions

Delete an ML model from OpenMetadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes
hard_deleteNo
recursiveNo
Behavior2/5

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

With no annotations provided, the description carries full burden but only states the basic action. It doesn't disclose critical behavioral traits: whether deletion is permanent/reversible, what happens to dependent resources, authentication requirements, error conditions, or rate limits. For a destructive operation, this is a significant gap.

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 zero wasted words. It's appropriately sized for a basic tool description and gets straight to the point without unnecessary elaboration.

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?

For a destructive tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is severely inadequate. It doesn't explain what happens after deletion, error handling, or the implications of the boolean parameters. The agent lacks critical context to use this tool safely and effectively.

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 parameters are undocumented in the schema. The description adds no information about the three parameters (model_id, hard_delete, recursive) - not explaining what they mean, their effects, or format requirements. This leaves the agent guessing about parameter usage.

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 ('Delete') and resource ('an ML model from OpenMetadata'), making the purpose unambiguous. However, it doesn't differentiate from sibling delete tools (e.g., delete_bot, delete_table) beyond specifying the resource type, which is somewhat implied by the tool name itself.

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 is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, permissions needed, or when to choose hard vs soft delete. It also doesn't reference related tools like update_ml_model or get_ml_model for context.

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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