Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Ask TypeSafe Jev yes/no, choice and score questions and receive structured model judgments.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
JEV_API__KEY标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"judgevet": {
"command": "uvx",
"args": [
"judgevet"
],
"env": {
"JEV_API__KEY": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add judgevet --env JEV_API__KEY=your-value -- uvx judgevet运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"judgevet": {
"command": "uvx",
"args": [
"judgevet"
],
"env": {
"JEV_API__KEY": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"judgevet": {
"type": "stdio",
"command": "uvx",
"args": [
"judgevet"
],
"env": {
"JEV_API__KEY": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 Alberto-Codes 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
你需要填写:JEV_API__KEY
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.23.0)于13天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Hunter Killer
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Liquidation walls, snapshot age, evidence labels and a public record. Managed OAuth.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
A typed Python client for TypeSafe's Jev (System One) judgment model, with CLI and optional MCP entry points. Use it to ask structured questions about content—for example, whether a support ticket concerns billing.
The state is your content. A question describes what to evaluate. A typed answer contains the model's values, which your application can inspect or compare with a local acceptance policy.
Unofficial and not affiliated with TypeSafe. The official TypeSafe Python SDK also provides typed questions and synchronous and asynchronous clients. judgevet adds a shared library, CLI and MCP contract with local policy evaluation. The library and CLI install without the MCP runtime.
Documentation status: draft. Start below or follow the first-judgment tutorial for environment setup, checkpoints and recovery steps.
Use Python 3.12 or newer. In a virtual environment:
python -m pip install 'judgevet==0.23.0'
judgevet --help
<!-- x-release-please-end -->
Supply a TypeSafe API key through your process environment or secret provider. This example reads JEV_API__KEY and passes it explicitly to the library. Do not put real keys in source or command arguments. The installation guide covers other installation methods.
This call sends the state, questions and selected model to the configured service. Send only content you are authorized to disclose. Read the data and credential guidance before using sensitive content. The example uses a synthetic ticket:
import os
from judgevet import HTTPSystemOneAdapter, Noul
with HTTPSystemOneAdapter(api_key=os.environ["JEV_API__KEY"]) as client: response = client.system_one( state="I was charged twice. Please help today.", questions={"billing": Noul(instructions="Is this about billing?")}, )
answer = response.nouls["billing"] print(f"Probability of billing: {answer.noul}") print(f"Resolved model: {response.model}") print(f"Input tokens: {response.usage.input_tokens}")
The context manager closes the HTTP client after the call. The answer remains available. If the import or call fails, use the tutorial's recovery steps or the troubleshooting guide.
A Noul value such as 0.85 reports the model's probability of yes. The actual value varies. It is not a boolean, a measured accuracy rate or an instruction to route the ticket. Noul has no separate confidence field. See the vendor's Noul definition.
The returned container also records the resolved model and token usage. Other question types let you select a label or evaluate an ordered rubric:
| Question | What its answer contains |
|---|---|
Noul | Probability of yes |
Choice | Selected label, per-label probabilities and confidence |
Score | Continuous score, rubric legend, per-level probabilities and confidence |
The vendor API reference defines those fields. The question-type explanation shows how to choose a type and distinguish score, confidence and probability.
A local policy can require a minimum value before your application accepts an answer. For example, 0.85 meets a minimum of 0.8 and fails one of 0.9. These are teaching thresholds, not production recommendations. A passing policy does not prove the model is correct or perform the application's next action. Try the first-policy tutorial with synthetic answers; it requires no key or network.
| Need | Start here |
|---|---|
| Learn one complete service call | First judgment |
| Call from an async application | Async Python guide |
| Use a mounted key or secret provider | Credential sources |
| Deploy in containers or serverless | Deployment guide |
| Correlate diagnostic events | Event contract and caller binding |
| Configure a proxy or private CA | Network configuration |
| Configure bounded retries | Retry limits |
| Handle service and transport failures | Library error handling |
| Evaluate typed or JSON policies in Python | Policy guide |
| Use shell commands, files or stdin | CLI inputs and CLI policies |
| Give an agent judgment tools | Optional MCP installation, connection and discovery |
| Look up types, options or schemas | Reference map |
| Understand thresholds and tradeoffs | Local policy explanation |
| Choose an entry point and own its lifecycle | Library-first architecture |
The CLI uses the same library and can return JSON or a policy exit status. The optional MCP server exposes ask_noul, ask_choice, ask_score and evaluate_policy over stdio. Follow the connection guide to install the extra and configure a host. These are alternative entry points; you do not need MCP to use Python or the CLI.
Exact documentation examples run against isolated installations with synthetic answers. Those checks establish local wiring, not model quality or calibration. Live evidence covers only the observed cases and resolved jev-1.13.0 model. The 429/529 error bodies remain unseen; other models and untouched fields remain unverified. The verification explanation separates local tests, vendor statements and live observations.
Diagnostics are quiet by default. Debug metadata excludes state and question text, but protocol errors, CLI error envelopes and arbitrary tracebacks can contain sensitive content. Review them before sharing. The security policy describes credential, disclosure and diagnostic limits.
Read the contribution policy before proposing work. Use the maintainer procedures for documentation checks, package verification and releases. Repository contribution rules define gates and commits. The verification ledger records which service claims a live call has verified.