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mlflow_registered_models_list

Read-only

Search and retrieve a list of registered MLflow models in Databricks. Filter, order, and paginate results to find specific models.

Instructions

Search registered models (POST /api/2.0/mlflow/registered-models/search).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNo
max_resultsNo
order_byNo
page_tokenNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations already provide readOnlyHint=true, and the description does not add behavioral context beyond that. No contradiction, but no extra value.

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 a single sentence with no waste, but it could include parameter details without becoming verbose.

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?

Despite having an output schema, the description lacks information on pagination, return format, and parameter semantics, making it incomplete for a list operation with 4 parameters.

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 does not explain any parameters (filter, max_results, order_by, page_token), leaving the agent without guidance on their meaning or usage.

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?

The description clearly states the action (search) and resource (registered models), and includes the HTTP method and endpoint, distinguishing it from sibling tools like get, create, etc.

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 like mlflow_registered_models_get, or on filtering, ordering, or pagination.

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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