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 garethbeaumo · MCP Server · ★ 71
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is originlab-mcp safe to install? View the security audit →
OriginLab MCP Server Use AI as your OriginLab assistant Connect OriginLab data analysis and visualization to Antigravity, Claude, Cursor, and other AI clients through the Model Context Protocol. Quick Start · Features · Examples · Client Configuration 简体中文 · English [!WARNING] Early v0.2 release - this project is still under active development. Features and APIs may change. It is suitable for testing and feedback, but not recommended for production use yet. What Is OriginLab MCP Server? OriginLab MCP Server is a bridge between AI clients and OriginLab. It lets you import data,
| Stars | 71 |
| Forks | 1 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 74.6784644256139/100 |
| Last Updated | 2026-08-27 |
| Created | 2026-03-07 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~16k |
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originlab-mcp is MCP Server for OriginLab Control OriginLab with natural language through AI clients via the Model Context Protocol.. It is categorized as a MCP Server with 71 GitHub stars.
originlab-mcp is primarily written in Python. It covers topics such as ai, automation, data-visualization.
You can find installation instructions and usage details in the originlab-mcp GitHub repository at github.com/garethbeaumo/originlab-mcp. The project has 71 stars and 1 forks, indicating an active community.
originlab-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to originlab-mcp on Agent Skills Hub include scitex-python, sudocode, AutoRedTeam-Orchestrator. 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.
Sources & who's responsible: