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openmetadata-mcp-server

list-ml-model-services

List ML model services from OpenMetadata with optional filters for fields, limit, pagination, and include status.

Instructions

List ML model services

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoComma-separated fields to include
limitNo
beforeNo
afterNo
includeNonon-deleted
Behavior1/5

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

With no annotations, the description must disclose behavioral traits, but it only says 'List ML model services'. It does not mention pagination, filtering, side effects, or typical list behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While extremely short, it is not concise because it omits essential information. A single sentence is insufficient for a 5-parameter tool with many siblings.

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

Completeness1/5

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

Given the complexity (5 parameters, many siblings, no output schema), the description is completely inadequate. It lacks details on return values, pagination, and usage context.

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

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 20% (only 'include' has a description). The description adds no meaning for the other 4 parameters (fields, limit, before, after), leaving them unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'List ML model services' states the verb and resource but is too vague to distinguish from other list-* tools like list-ml-models. It does not clarify what an ML model service is compared to related entities.

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

Usage Guidelines1/5

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. There is no mention of context, prerequisites, or when not to use it.

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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