Jev

Name: Jev
Author: Aitejiu

by Aitejiuversion 0.1.2i★ –iRuns on your computer

Registry nameio.github.Aitejiu/jev

Install

Overview

Description from the maker (original English)

Jev judgments for agents: injection scanning, shell risk gating, ranking

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other
You need to fill in: TYPESAFE_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: TYPESAFE_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "jev": {
      "command": "uvx",
      "args": [
        "jev-mcp"
      ],
      "env": {
        "TYPESAFE_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add jev --env TYPESAFE_API_KEY=your-value -- uvx jev-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "jev": {
      "command": "uvx",
      "args": [
        "jev-mcp"
      ],
      "env": {
        "TYPESAFE_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "jev": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "jev-mcp"
      ],
      "env": {
        "TYPESAFE_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

TYPESAFE_API_KEY secret required env
TypeSafe API key used for Jev judgments

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Aitejiu, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32568 servers in this category
Published to the registry more recently than 46% of the other 32568 servers in this category
Basic checks equal to or better than 100% of the other 32568 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Jev safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: TYPESAFE_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.2) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, FlyDocs

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Runs on your computer★ 440MITUpdated 14 months ago

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Remote Desktop Commander

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday

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FlyDocs

by flydocs.ai

Spec-driven development workflow: your agent works issues, sessions and the status lifecycle.

Runs on your computerUpdated today

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

jev-harness-lab

用 TypeSafe Jev（System One 决策模型）做 agent harness 工程实验的技术仓库：评测框架、基准报告、可运行的集成工具（MCP server、skill router）。

安装

① MCP server（给 agent 加三个 Jev 工具）

用 uv 一条命令，无需克隆：

# 从 PyPI

uvx jev-mcp

直接从 GitHub 源码运行

uvx --from git+https://github.com/Aitejiu/jev-harness-lab jev-mcp

配置到任意 MCP 客户端（opencode / Claude Desktop 等）：

"mcp": { "jev": { "type": "local", "command": ["uvx", "--from", "git+https://github.com/Aitejiu/jev-harness-lab", "jev-mcp"], "environment": { "TYPESAFE_API_KEY": "your-key" }, "enabled": true } } }

提供的工具：

工具	作用
`scan_injection`	扫描工具/网页/邮件输出里的注入指令，返回 block / review / pass
`bash_risk`	shell 命令四维风险打分（破坏性 / 触密 / 外发 / 不可逆），返回 deny / review / allow
`rank_candidates`	候选片段按相关性打分排序（RAG 精排）

MCP Registry 归属标记（勿改格式）：

mcp-name: io.github.Aitejiu/jev

② Agent skill（让 Jev 帮主模型选 skill）

npx skills add Aitejiu/jev-harness-lab --skill jev-skill-router

用 Jev 把任务路由到已安装的 skill，并且只加载选中那一个的完整指令，避免把整个 skill 目录塞进主模型上下文。技能页：https://www.skills.sh/aitejiu/jev-harness-lab/jev-skill-router

环境变量

两个集成都需要：

export TYPESAFE_API_KEY=<your-key>

├── examples/ # 最小可运行示例（三原语、state、路由、护栏、抽取） ├── eval/ # 评测框架：11 个基准脚本 + 24 份报告 + 原始结果 │ └── datasets/ # 手工构造的数据集（如 130 条 shell 命令风险集） ├── integrations/ │ └── opencode/ # opencode 插件：jev_route_skill（skill 门控） ├── skills/ │ └── jev-skill-router/ # 可安装的 agent skill（SKILL.md + 独立脚本，skills.sh） ├── docs/ │ └── REPORT.md # 技术评估报告（数据与结论） ├── src/jev_mcp/ # MCP server 包（PyPI: jev-mcp） ├── pyproject.toml # Python 打包配置（console script: jev-mcp） ├── server.json # MCP Registry 元数据（mcp-name: io.github.Aitejiu/jev） ├── mcp_server.py # 兼容 shim：不安装也可 python mcp_server.py 运行 ├── skill_router.py # 本地 skill 目录路由（Jev 选择并加载 SKILL.md） ├── common.py # .env 加载 + 共享 client └── requirements.txt

快速开始

uv venv --python 3.12 .venv

uv pip install -r requirements.txt echo "TYPESAFE_API_KEY= " > .env

# 最小示例

.venv/bin/python examples/quickstart.py .venv/bin/python examples/noul.py # criteria 对判断的影响 .venv/bin/python examples/routing.py # 阈值路由

跑评测（示例：skill router，501 个真实 skills）

.venv/bin/python eval/run_skillretbench.py --variant hybrid --per-setting 100

评测结果缓存在 eval/results/*.jsonl（已随仓库提交，可 --report-only 直接出报告）；原始数据集在 eval/data/，需按 eval/README.md 的说明下载（已 gitignore）。

集成工具

MCP server

.venv/bin/python mcp_server.py   # stdio

工具	作用	输入 → 输出
`scan_injection`	扫描工具输出中的注入指令	tool_output → `action`(block/review/pass) + 概率
`bash_risk`	shell 命令四维风险打分	command → `action`(deny/review/allow) + 破坏性/触密/外发/不可逆分数
`rank_candidates`	候选片段相关性重排	query + candidates(≤10) → 排序后的 index/score

接入 opencode 的配置示例（项目 .opencode/opencode.json）：

"mcp": { "jev": { "type": "local", "command": ["/absolute/path/to/jev-harness-lab/.venv/bin/python", "/absolute/path/to/jev-harness-lab/mcp_server.py"], "enabled": true } } }

Skill router

.venv/bin/python skill_router.py --query "帮我查飞书文档" --load

opencode 插件在 integrations/opencode/jev-skill-router.ts：注册 jev_route_skill(task) 工具，请求进来时用 Jev 从本地 skill 目录选出最合适的一个并返回其完整指令。参考做法是配合 agent.build.tools.skill = false 关闭内置 skill 工具，使主模型上下文不再携带整个 skill 目录。

主要基准结果（详见 docs/REPORT.md）

任务	数据	结果
间接注入检测	InjecAgent 1,105 条	阈值 0.10：P/R 100%，良性误报 0%
检索重排	BEIR SciFact 900 对	BM25 → Jev：MRR 0.622 → 0.843，Hit@1 50% → 78.3%
意图分类	SNIPS / Banking77	7 类 97.9% / 77 类 80.3%
工具目录路由	MetaTool 199 工具	相似干扰 k=5 96.5%
Skill router	SkillRetBench 501 库	hybrid 架构 R@1 75.8%（最强基线 38.0%）
命令风险门控	自建 130 条	危险拦截 100%、正常放行 98.2%
模型难度路由	RouterBench	51%（无信号，负结果）
轨迹失败归因	Who&When 1,403 步	AUROC 0.56（负结果）

总计约 22,500 次 API 调用、52.2M input tokens、$2.19。

说明

所有 benchmark 使用公开数据集；原始结果 JSONL 已提交，报告可复现。
部分官方基线为模拟实现（在报告中已标注）。
Jev 已知限制：choice 最多 255 个选项、仅文本输入、state+questions 共享约 32k tokens、英语为主。
版本：jev-1.13.0，模型升级后建议重新评测。

Jev

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

FlyDocs

README from the maker

jev-harness-lab

安装

① MCP server（给 agent 加三个 Jev 工具）

直接从 GitHub 源码运行

② Agent skill（让 Jev 帮主模型选 skill）

环境变量

目录

快速开始

跑评测（示例：skill router，501 个真实 skills）

集成工具

MCP server

Skill router

主要基准结果（详见 docs/REPORT.md）

说明