by MarcelRoozekrans · MCP Server · ★ 70
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 70 |
| Forks | 17 |
| Language | C# |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69/100 |
| Open Issues | 1 |
| Last Updated | 2026-11-05 |
| Created | 2026-03-06 |
| Platforms | claude-code, dotnet, mcp |
| Est. Tokens | ~21k |
Roslyn CodeLens MCP Server A Roslyn-based MCP server that gives AI agents deep semantic understanding of .NET codebases — type hierarchies, call graphs, DI registrations, diagnostics, refactoring, and more. <!-- mcp-name: io.gi
Looking for a roslyn-codelens-mcp alternative? If you're comparing roslyn-codelens-mcp with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Cross-language-safe code-intelligence MCP for AI agents: 13 languages, family-gated call graph, blast radius,
Bring Claude Code to Visual Studio 2026: A native diff with accept/reject, a live debugger Claude can drive au
The code quality toolkit for the agentic AI era. Find dead code, clones, and scaffolding across 15 languages.
A suite of MCP tools for AIs to analyze and modify C# solutions with high signal, Roslyn powered context.
A Model Context Protocol (MCP) server that provides advanced code analysis and reasoning capabilities powered
Codebase Context gives AI agents understanding of your codebase through semantic code search, team conventions
Explore other popular mcp server tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: