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 portofcontext · MCP Server · ★ 273
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is pctx safe to install? View the security audit →
pctx The open source framework to connect AI agents to tools and mcp with Code Mode — T R U S T E D B Y — Install bash Homebrew brew install portofcontext/tap/pctx cURL curl --proto '=https' --tlsv1.2 -LsSf https://raw.githubusercontent.com/portofcontext/pctx/main/install.sh |
| Stars | 273 |
| Forks | 35 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 72.6277011628717/100 |
| Open Issues | 11 |
| Last Updated | 2026-08-31 |
| Created | 2025-11-04 |
| Platforms | mcp, rust |
| Est. Tokens | ~23k |
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pctx is pctx is the execution layer for agentic tool calls. It auto-converts agent tools and MCP servers into code that runs in secure sandboxes for token-efficient workflows.. It is categorized as a MCP Server with 273 GitHub stars.
pctx is primarily written in Rust. It covers topics such as ai-agents, api, infrastructure.
You can find installation instructions and usage details in the pctx GitHub repository at github.com/portofcontext/pctx. The project has 273 stars and 35 forks, indicating an active community.
pctx is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to pctx on Agent Skills Hub include MCPJungle, MakeMoneyWithAI, daymon. 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: