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Optuna MCP Server

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by optuna

plot_parallel_coordinate

Generate a parallel coordinate plot to visualize the relationships between hyperparameters and objective values in optimization studies.

Instructions

Return the parallel coordinate plot as an image.

    Args:
        params:
            Parameter list to visualize. The default is all parameters.
        target:
            An index to specify the value to display. To plot nth objective value, set this to n.
            Note that this is 0-indexed, i.e., to plot the first objective value, set this to 0.
        target_name:
            Target’s name to display on the axis label and the legend.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsNo
targetNo
target_nameNoObjective Value
Behavior2/5

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

No annotations are provided, and the description fails to disclose behavioral traits such as read-only nature, side effects, or permissions. It only states it returns an image.

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 structured as a docstring with Args section. It is informative but slightly verbose; each sentence is useful. Could be more concise, but performs well.

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

Completeness4/5

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

For a plot generation tool, the description sufficiently explains return type (image) and parameters. No output schema, but the basic behavior is conveyed. Missing advanced context like image format or interaction with other tools.

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

Parameters4/5

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

With 0% schema coverage, the description adds significant meaning: explains 'params' default is all parameters, 'target' is 0-indexed index for objective value, and 'target_name' is for axis label. This compensates well for missing schema descriptions.

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 tool returns a parallel coordinate plot as an image, specifying verb and resource. However, it does not differentiate from sibling plot tools like plot_contour or plot_slice, lacking explicit distinction.

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. The description only explains parameters without context on appropriate scenarios or exclusions.

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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