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 modelcontextprotocol · MCP Server · ★ 1.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is swift-sdk safe to install? View the security audit →
MCP Swift SDK Official Swift SDK for the [Model Context Protocol][mcp] (MCP). Overview The Model Context Protocol (MCP) defines a standardized way for applications to communicate with AI and ML models. This Swift SDK implements both client and server components according to the [2025-11-25][mcp-spec-2025-11-25] (latest) version of the MCP specification. Table of contents Requirements Installation Client Usage Basic Client Setup Transport Options for Clients Tools Resources Prompts Completions Sampling Elicitation Roots Logging Error Handling Cancellation Progress Tracking Advanced Client Features Server Usage Basic Server Setup Tools Resources Prompts Completions Sampling Elicitations Roots Logging Progress Tracking Initialize Hook Graceful Shutdown Transports Platform Availability [Debugging an
| Stars | 1,492 |
| Forks | 243 |
| Language | Swift |
| Category | MCP Server |
| Quality Score | 69.7617119358062/100 |
| Open Issues | 109 |
| Last Updated | 2026-05-07 |
| Created | 2025-02-05 |
| Platforms | cli, mcp |
| Est. Tokens | ~43k |
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swift-sdk is The official Swift SDK for Model Context Protocol servers and clients.. It is categorized as a MCP Server with 1.5k GitHub stars.
swift-sdk is primarily written in Swift. It covers topics such as mcp, swift.
You can find installation instructions and usage details in the swift-sdk GitHub repository at github.com/modelcontextprotocol/swift-sdk. The project has 1.5k stars and 243 forks, indicating an active community.
The top alternatives to swift-sdk on Agent Skills Hub include apple-docs-mcp, cupertino, Wax. 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: