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mlflow-mcp-server

search-registered-models

Read-only

Find registered MLflow models by applying filter expressions, sorting, and pagination to retrieve specific model versions.

Instructions

Search registered models

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoFilter expression (e.g. "name LIKE 'foo%'")
maxResultsNo
orderByNo
pageTokenNo
Behavior2/5

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the behavioral safety is partially covered. However, the description adds no extra context about pagination, result ordering, or potential limitations. It does not contradict annotations but adds no value.

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

Conciseness3/5

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

The description is a single sentence, which is concise. However, it is too terse and lacks essential details, making it under-specified rather than efficiently informative.

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 4 parameters, no output schema, and many sibling search tools, the description is severely incomplete. It omits important context like pagination, filtering syntax, ordering options, and return value structure, leaving the agent without sufficient guidance.

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 low (25%), with only the 'filter' parameter having a helpful example. The tool description does not explain any parameters, leaving the agent to rely solely on the sparse schema. It fails to compensate for the low coverage.

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 'Search registered models' is a verb+resource statement, but it is extremely vague and does not distinguish this tool from numerous sibling search tools like 'search-experiments' or 'search-model-versions'. It lacks specificity about the scope or filtering capabilities.

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 such as 'search-model-versions' or 'get-registered-model'. There are no when-to-use or when-not-to-use instructions.

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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