# Optuna

> Use this tool when you need to automate hyperparameter optimization for machine learning models or other applications, and want to analyze and visualize the results to inform decision-making. Optuna solves problems of manual parameter tuning, supporting single and multi-objective optimization with various samplers and persistent storage options. It takes in optimization study configurations and parameter values as inputs, and outputs comprehensive visualizations, including optimization history and Pareto fronts, to guide further analysis and improvement.

Canonical page: https://skillsregistry.net/skills/optuna  
JSON: https://api.skillsregistry.net/v1/skills/optuna

## Description

This MCP server provides automated optimization and analysis capabilities using Optuna, a hyperparameter optimization framework. Built by Preferred Networks, it offers tools for creating and managing optimization studies with various samplers (TPE, NSGA-II, Random, GP), suggesting parameter values through ask/tell interfaces, and generating comprehensive visualizations including optimization history, Pareto fronts, parameter importance plots, and contour maps. The implementation supports both single and multi-objective optimization with persistent storage options, includes an integrated Optuna Dashboard for interactive analysis, and enables AI assistants to automatically tune hyperparameters for machine learning models, optimize input/output parameters for other MCP tools, or conduct systematic parameter searches across any optimization problem space.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/optuna)
- **Repository:** <https://github.com/optuna/optuna-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "optuna"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/optuna` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/optuna/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt