No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by optuna · MCP Server · ★ 84
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Optuna MCP Server A Model Context Protocol (MCP) server that automates optimization and analysis using Optuna. Use Cases The Optuna MCP Server can be used in the following use cases, for example. Automated hyperparameter optimization by LLMs Interactive analysis of Optuna's optimization results via chat interface Optimize input and output of other MCP tools For details, see the Examples section. Installation The Optuna MCP server can be installed using or Docker. This section explains how to install the Optuna MCP server, using Claude Desktop as an example MCP client. Usage with uv Before starting the installation process, install from Astral. Then, add the Optuna MCP server configuration to the MCP client. To include i
| Stars | 84 |
| Forks | 25 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.5875431298494/100 |
| Last Updated | 2026-08-05 |
| Created | 2025-06-10 |
| Platforms | mcp, python |
| Est. Tokens | ~16k |
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optuna-mcp is The Optuna MCP Server is a Model Context Protocol (MCP) server to interact with Optuna APIs.. It is categorized as a MCP Server with 84 GitHub stars.
optuna-mcp is primarily written in Python. It covers topics such as hyperparameter-optimization, llm, mcp.
You can find installation instructions and usage details in the optuna-mcp GitHub repository at github.com/optuna/optuna-mcp. The project has 84 stars and 25 forks, indicating an active community.
optuna-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to optuna-mcp on Agent Skills Hub include zettelkasten-mcp, youtube-connector-mcp, mcp-gateway. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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