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Optuna MCP Server

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Python
Hybrid

set_metric_names

Assign labels to each objective value to distinguish them in multi-objective optimization. Specify a list of metric names matching the number of objectives.

Instructions

Set metric_names. metric_names are labels used to distinguish what each objective value is.

    Args:
        metric_names:
            The list of metric name for each objective value.
            The length of metric_names list must be the same with the number of objectives.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metric_namesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
study_nameYes
sampler_nameNoThe name of the sampler used in the study.
directionsNoThe optimization directions for each objective.
metric_namesNoThe metric names for each objective.

Tool Definition Quality

B3.4/5.0
Behavior2/5

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

No annotations provided, so description carries full burden. It only mentions the length constraint for metric_names, omitting details like overwrite behavior or prerequisites.

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?

Description is short and front-loaded with the core action. Minor verbosity from docstring format (Args) but overall efficient.

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?

Adequate for a simple 1-parameter tool, but lacks side effects, return value, or prerequisites. Output schema exists but not described.

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?

Schema has 0% coverage, but description adds purpose and the length constraint, significantly clarifying the parameter beyond schema.

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 'Set metric_names' and explains their role as labels for objective values. This distinguishes it from sibling tools like get_metric_names that retrieve them.

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 like set_trial_user_attr or when not to use it. Sibling tools exist but no explicit context.

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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