MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
作者提供的说明(英文原文)
Local-first PHI detection for source code, LLM prompts, logs, and analytics
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。
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": {
"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。
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 Abidit 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。
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(0.2.0)于2个月前进入注册中心。
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 Node.js。
同一分类中有:MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
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
An MCP server retrieving transcripts of YouTube videos
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.
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 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.
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.
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.
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.
Nothing to clone or build. Your MCP client runs it on demand:
npx -y phi-guard-mcp --version
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.
Copy-paste one of the following. The npm form needs no paths and is the one to hand to someone else.
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"] } } }
~/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/mcp.json in the project, or ~/.cursor/mcp.json globally:
{
"mcpServers": { "phi-guard": { "command": "npx", "args": ["-y", "phi-guard-mcp"] } } }
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.
redact_suggestTakes 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:
| Type | Confidence | Matches |
|---|---|---|
ssn | 0.95 | 123-45-6789 |
mrn | 0.90 | MRN-12345, MRN: 12345 |
dob | 0.85 | DOB: 01/01/1980, born 3/14/75 |
name | 0.80 | Patient John Doe (captures John Doe) |
phone | 0.75 | 555-867-5309, (555) 867 5309 |
email | 0.70 | jane.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_codeWalks 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:
| Category | Matches |
|---|---|
| LLM providers | openai, anthropic, bedrock |
| Console | console.log, console.error, console.warn |
| Loggers | logger., winston, pino |
| Analytics | .track( |
| Error reporting | capture(, captureException(, captureMessage( |
Languages scanned — the complete list:
| Extension | Language |
|---|---|
.ts, .tsx | TypeScript |
.js, .jsx | JavaScript |
.py | Python |
.go | Go |
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')
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.
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:
scan_code resultredact_suggest omits every matched value and the original by defaultincludeMatchedValues: true actually returns themWhat 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
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