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 posit-dev · MCP Server · ★ 196
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcptools safe to install? View the security audit →
mcptools mcptools implements the Model Context Protocol in R. There are two sides to mcptools: R as an MCP server: <img src="man/figures/rasaserver.png" alt="A system architecture diagram showing three main components: Client (left), Server (center), and Session (right). The Client box lists AI coding assistants including Claude Desktop, Claude Code, Copilot Chat in VS Code, and Positron Assistant. The Server is initiated with and contains tools for R functions like reading package documentation, running R code, and inspecting global environment objects. Sessions can be configured with and can optionally connect to interactive R sessions, with two exampl
| Stars | 196 |
| Forks | 21 |
| Language | R |
| Category | MCP Server |
| Quality Score | 64.8888325099817/100 |
| Open Issues | 24 |
| Last Updated | 2026-09-17 |
| Created | 2025-03-26 |
| Platforms | mcp |
| Est. Tokens | ~17k |
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mcptools is Model Context Protocol For R. It is categorized as a MCP Server with 196 GitHub stars.
mcptools is primarily written in R. It covers topics such as mcp-server, r.
You can find installation instructions and usage details in the mcptools GitHub repository at github.com/posit-dev/mcptools. The project has 196 stars and 21 forks, indicating an active community.
The top alternatives to mcptools on Agent Skills Hub include ClaudeR. 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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