MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
Descripción del creador (original en inglés)
Local-first PHI detection for source code, LLM prompts, logs, and analytics
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"phi-guard": {
"command": "npx",
"args": [
"-y",
"phi-guard-mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add phi-guard -- npx -y phi-guard-mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"phi-guard": {
"command": "npx",
"args": [
"-y",
"phi-guard-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"phi-guard": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"phi-guard-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub Abidit, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.2.0) llegó al registro hace 2 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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
Solo se muestra el principio; lee el resto en el repo