原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Phi Guard MCP

作者:Abidit版本 0.2.0i★ –i在你的电脑上运行
注册名称io.github.Abidit/phi-guard-mcp

安装

概览

作者提供的说明(英文原文)

Local-first PHI detection for source code, LLM prompts, logs, and analytics

PopNx MCP 的三大支柱

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。

它能帮你做什么

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。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 营销与社交媒体

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • npm已安装 Node.js(用于 npx 命令)

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": {
    "phi-guard": {
      "command": "npx",
      "args": [
        "-y",
        "phi-guard-mcp"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add phi-guard -- npx -y phi-guard-mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "phi-guard": {
      "command": "npx",
      "args": [
        "-y",
        "phi-guard-mcp"
      ]
    }
  }
}

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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "phi-guard": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "phi-guard-mcp"
      ]
    }
  }
}

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。

它能做什么

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。

基础检查i

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

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 Abidit 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    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“营销与社交媒体”中的其他服务器比较i

  • 星标数多于此分类中其他 2982 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 2982 个服务器的 36% 更新
  • 基础检查结果等于或优于此分类中其他 2982 个服务器的 100%

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。

常见问题

Phi Guard MCP 安全吗?

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。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

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)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.2.0)于2个月前进入注册中心。

哪些应用可以使用它?

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 Node.js。

有什么可以替代它?

同一分类中有:MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer

替代选择

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 YouTube Transcript

作者:jkawamoto

An MCP server retrieving transcripts of YouTube videos

在你的电脑上运行★ 495MIT10天前更新
安装

Google Analytics MCP

作者:surendranb

GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.

在你的电脑上运行★ 244MIT10天前更新
安装

AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.

在云端运行★ 137MIT10天前更新
安装

作者的 README

打开 README

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。

phi-guard-mcp

A local-first MCP server that catches PHI (protected health information) flowing into LLM prompts, log statements, and analytics calls — in your source code, before it ships.

The server itself makes no network calls: it runs as a local stdio process and never uploads your code. What it returns is a different matter. Findings go back to whatever MCP client launched it, so if that client is a hosted assistant, the findings enter that model's context. Matched PHI values are masked out of results by default for exactly this reason — see Security boundary.

Why

The risky moment in a healthcare codebase is rarely the database. It's the line where a patient record gets interpolated into a prompt, a console.log, or an analytics event. Those lines look harmless in review and never show up in infrastructure scanning, because nothing is misconfigured — the code is just doing what it says.

Demo

Eleven seconds of the walkthrough: scan_code flags line 15 — a patient name and diagnosis interpolated into an openai.responses.create call — and redact_suggest masks the same values out of a raw prompt. Every value shown is synthetic.

A linter for one class of mistake. Not a HIPAA certification, not a compliance

attestation, and not a dataflow analyzer — see

What this is NOT.

Install

Requires Node.js 22 or newer. The entrypoint uses JSON import attributes (with { type: "json" }), so older runtimes will not start it. Verified on Node 22.18.0, 24.2.0, and 26.8.2; Node 20 and below are unsupported and untested.

From npm (recommended)

Nothing to clone or build. Your MCP client runs it on demand:

npx -y phi-guard-mcp --version
From source
git clone https://github.com/Abidit/phi-guard-mcp.git

cd phi-guard-mcp npm ci

npm ci runs the prepare script, which builds dist/. There is no separate build step to forget. npm install works too; npm ci is the reproducible one because it installs exactly what package-lock.json pins.

MCP configuration

Copy-paste one of the following. The npm form needs no paths and is the one to hand to someone else.

Claude Code

One command, project scope:

claude mcp add phi-guard -- npx -y phi-guard-mcp

Or commit a .mcp.json at your project root:

{

"mcpServers": { "phi-guard": { "command": "npx", "args": ["-y", "phi-guard-mcp"] } } }

Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows:

{

"mcpServers": { "phi-guard": { "command": "npx", "args": ["-y", "phi-guard-mcp"] } } }

Cursor

.cursor/mcp.json in the project, or ~/.cursor/mcp.json globally:

{

"mcpServers": { "phi-guard": { "command": "npx", "args": ["-y", "phi-guard-mcp"] } } }

Running a local clone instead of the npm release

Point the client at the build output. Use an absolute path unless you are certain your client launches the server with the project root as its working directory:

{

"mcpServers": { "phi-guard": { "command": "node", "args": ["/absolute/path/to/phi-guard-mcp/dist/index.js"] } } }

The .mcp.json committed in this repo uses the relative form (dist/index.js) so the repo can dogfood its own server after npm ci.

Whichever form you use: restart the client, or run /mcp in Claude Code and reconnect phi-guard. A rebuild alone will not reach an already-running stdio process.

Tools

redact_suggest

Takes a raw text snippet — a log line, a prompt, an error message — detects PHI-shaped values, and returns a redacted version alongside what it found.

Input

{ "text": "Patient John Doe (MRN-12345), DOB: 01/01/1980" }

Output

{

"redacted": "Patient [NAME] ([MRN]), [DOB]", "detected": [ { "type": "mrn", "confidence": 0.9, "start": 18, "end": 27 }, { "type": "dob", "confidence": 0.85, "start": 30, "end": 45 }, { "type": "name", "confidence": 0.8, "start": 8, "end": 16 } ] }

The matched values are not echoed back by default, and neither is the unredacted original. A tool result flows straight into the context of whatever model called it, so repeating the raw PHI there would undo the point of the tool. start/end are offsets into the original text, which is enough to locate a match without restating it.

detected is ordered by pattern, not by position.

Pass includeMatchedValues: true when you genuinely need the raw values (a local CLI, a test harness) and detected[].value plus original come back:

{ "text": "Patient John Doe (MRN-12345)", "includeMatchedValues": true }

Patterns and their confidence scores:

TypeConfidenceMatches
ssn0.95123-45-6789
mrn0.90MRN-12345, MRN: 12345
dob0.85DOB: 01/01/1980, born 3/14/75
name0.80Patient John Doe (captures John Doe)
phone0.75555-867-5309, (555) 867 5309
email0.70jane.roe@example.com

That table is the complete list. The patterns start deliberately narrow — a false positive that trains someone to ignore the tool is worse than a missed match.

scan_code

Walks a directory and flags lines where a sensitive-looking identifier appears on the same line as a risky sink.

Sensitive identifiers — the complete list:

patient, diagnosis, dob, ssn, mrn, birthdate, medicalrecord

Matching is case-insensitive and bounded by non-letters, so patient_name matches while outpatient and inpatient do not.

Sink categories — the complete list:

CategoryMatches
LLM providersopenai, anthropic, bedrock
Consoleconsole.log, console.error, console.warn
Loggerslogger., winston, pino
Analytics.track(
Error reportingcapture(, captureException(, captureMessage(

Languages scanned — the complete list:

ExtensionLanguage
.ts, .tsxTypeScript
.js, .jsxJavaScript
.pyPython
.goGo

Detection is purely lexical, so language support means "these file extensions are read". There is no parser and no type information for any of them.

Skips node_modules, dist, build, coverage, out, .next, .turbo, and any dotfile or dot-directory. Whole-line // and # comments are skipped, so a file that discusses PHI handling in prose doesn't trip the scanner on its own documentation.

Given the operative lines of test/fixtures/leaky-example.ts:

const prompt = await openai.responses.create({ input: `Patient: ${patient.name}, diagnosis: ${patient.diagnosis}` });

console.log("Sending patient prompt to LLM:", prompt);

Input

{ "path": "/abs/path/to/repo/test/fixtures" }

Output — excerpt. That directory returns 9 findings in total: 2 from this file, and 7 from the positive fixtures described under Evaluation.

[

{ "file": "/abs/path/to/repo/test/fixtures/leaky-example.ts", "line": 7, "severity": "high", "issue": "Sensitive-looking identifier passed to a risky sink (LLM call, logger, or analytics)", "snippet": "const prompt = await openai.responses.create({ input: Patient: ${patient.name}, diagnosis: ${patient.diagnosis} });" }, { "file": "/abs/path/to/repo/test/fixtures/leaky-example.ts", "line": 8, "severity": "high", "issue": "Sensitive-looking identifier passed to a risky sink (LLM call, logger, or analytics)", "snippet": "console.log(\"Sending patient prompt to LLM:\", prompt);" } ]

file is built by joining path with the entry name, so it comes back in whatever form you passed in: absolute in, absolute out. severity is always "high" — there is one rule, so there is one severity.

snippet is the offending line with any literal PHI masked, for the same reason redact_suggest withholds matched values: the finding is going into a model's context. Identifier names like patient.diagnosis are not literal values, match no PHI pattern, and stay visible — they are the actionable part.

An empty array means every eligible source file discovered under path was read successfully and no line matched both conditions. "Eligible" and "discovered" are load-bearing: files with an unsupported extension, and anything under a skipped or dot-prefixed directory, are never opened.

A scan that cannot read a directory or a file fails with a named error rather than returning a shorter list, because a partial result reads as a clean result:

No such path: "/nope". Pass an absolute path to a directory that exists on the

machine running this server. (ENOENT: no such file or directory, stat '/nope')

The same-line rule

Both conditions must hold on the same physical line. This is the single most important thing to understand about the scanner, in both directions.

It is what keeps it quiet. On this repo's own src/ — which is dense with the words patient, diagnosis, mrn, and ssn inside its pattern definitions — it reports zero findings.

It is also the main reason it misses things. This leaks and is not flagged:

const value = patient.diagnosis;   // sensitive identifier, no sink

console.log("audit", value); // sink, no sensitive identifier

There is no dataflow analysis, no variable tracking, and no cross-line correlation. A worked example lives in test/fixtures/known-limitations/split-across-lines.ts, and the test suite scans it on every run so the gap stays visible rather than forgotten.

Evaluation

npm run verify

One command: typecheck (src/ and test/ under strict mode), build, then the fixture suite against the real server over stdio.

The current result:

**Detected all 7 representative leak fixtures and flagged none of 5 clean

fixtures.**

Those fixtures are all in test/fixtures/, so you can read them rather than take this on faith.

The 7 leak fixtures cover 5 sink categories (OpenAI, Anthropic, Sentry, Winston, analytics) and 2 languages (TypeScript, Python). They include snake_case identifiers (patient_name, patient_diagnosis), which a naive word-boundary regex misses and which is the dominant naming convention in Python and Go, and a hardcoded-literal fixture that verifies scan_code masks literal PHI out of the snippet it returns.

The 5 clean fixtures include code that discusses PHI policy in comments and prose without ever leaking it, and code that legitimately handles patient records without sending them anywhere risky.

The suite also asserts:

  • the README's worked example still produces exactly 2 findings, on lines 7 and 8
  • no raw literal PHI appears anywhere in a scan_code result
  • redact_suggest omits every matched value and the original by default
  • includeMatchedValues: true actually returns them
  • a missing path and a file-instead-of-directory each return a named error

What this number is not. It is a fixture suite of 12 hand-written files that this project wrote to exercise its own rules. It is not a detection rat

只显示了开头部分;其余内容请在仓库中阅读

问 PopNx