by aplavin · MCP Server · ★ 83
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/aplavin/julia-mcp.git
claude mcp add --scope user julia -- uv run --directory /any_directory/julia-mcp python server.pyTaken from the project's README on GitHub.
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 | 83 |
| Forks | 17 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 74/100 |
| Open Issues | 4 |
| Last Updated | 2026-08-22 |
| Created | 2026-02-01 |
| Platforms | mcp, python |
| Est. Tokens | ~6k |
julia-mcp MCP server that gives AI assistants access to efficient Julia code execution. Avoids Julia's startup and compilation costs by keeping sessions alive across calls, and persists state (variables, functions, loaded packages) between them — so each iteration is fast.
These tools work well together with julia-mcp for enhanced workflows:
Looking for a julia-mcp alternative? If you're comparing julia-mcp with other mcp server tools, these 4 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
MCP server giving AI agents full access to Julia's runtime via a live Gate — code execution, introspection, de
Skill + MCP server to turn your agent into an RLM. Load context, iterate with search/code/think tools, converg
self-extending sandboxed agentic harness. a loop, two tools and an llm
Sage Mode for F# development — REPL with solution or project loading, Live Testing for FREE, Hot Reload, and s
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: