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mlflow-mcp-server

by us-all

get-experiment-by-name

Retrieve full details of an MLflow experiment by providing its name.

Instructions

Get experiment details by name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
experimentNameYesExperiment name
Behavior2/5

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

No annotations provided, so the description carries full burden. It only says 'Get', indicating a read operation, but no further behavioral traits like error handling, authentication needs, or idempotency. Lacks depth for a reliable agent selection.

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 concise sentence with no wasted words. It is front-loaded with the key action. While very brief, it earns its place for a simple tool, though more detail could be added without harming conciseness.

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

Completeness3/5

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

For a simple getter with one parameter and no output schema, the description is minimally adequate. However, it lacks context about return format or differentiation from the sibling tool get-experiment. Slightly below what is needed for full completeness.

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

Parameters3/5

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

The schema covers 100% of parameters with a clear description ('Experiment name'). The description adds 'by name', which reinforces the parameter's role but doesn't add new semantic context. Baseline 3 is appropriate.

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 (Get), resource (experiment details), and method (by name). It distinguishes from siblings like get-experiment (likely by ID) and search-experiments (list). However, 'experiment details' is vague and could be more specific.

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. For example, it doesn't clarify using this when you have the experiment name, or recommend get-experiment if the ID is available. This omission reduces agent decision quality.

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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