by jkudish · MCP Server · ★ 569
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx -y @jkudish/jev-mcp
claude mcp add jev -- npx -y @jkudish/jev-mcpTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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.
One of 11 tools we ran end to end on the same task for our TypeSafe Jev page (2026-10-11).
Our pick (To use Jev from any agent, as MCP tools): Registered with one claude mcp add and answered its first call: one claim verified, one contradicted. There is no command-line example; you call it from an agent.
Result: its quick start ran on a Jev key alone · 3 steps beyond the README · 44 s to install and run
| Stars | 569 |
| Forks | 66 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69/100 |
| Open Issues | 4 |
| Last Updated | 2026-10-30 |
| Created | 2026-09-17 |
| Platforms | mcp, node |
| Est. Tokens | ~15k |
Jev MCP Fast, cheap, typed judgments from TypeSafe's Jev model, as MCP tools. Give your agent three judgment tools: checks claims against evidence, judges content before it enters context, ranks candidates by meaning with no embeddings. Each call returns verdicts with probability distributions and confidence in roughly 150 to 500 ms, for a fraction of a cent.
These tools work well together with jev-mcp for enhanced workflows:
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: