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PopNx MCPServidores MCP para quienes usan IA · explicados con palabras sencillas

Phi Guard MCP

de Abiditversión 0.2.0i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.Abidit/phi-guard-mcp

Instalar

Resumen

Descripción del creador (original en inglés)

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

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en tu computadora
  • Transporte: stdio
  • Categoría: Marketing y redes sociales

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

  • npmNode.js instalado (para el comando npx)

¿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-mcp

Abrir en Cursor

O copia la configuración tú

Pégalo 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.

Abrir en VS Code

O copia la configuración 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.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub Abidit, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Marketing y redes socialesi

  • Más estrellas que el 0% de los otros 2976 servidores de esta categoría
  • Publicado en el registro más recientemente que el 36% de los otros 2976 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 2976 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro Phi Guard MCP?

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.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

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).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (0.2.0) llegó al registro hace 2 meses.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.

¿Qué puedo usar en su lugar?

En la misma categoría: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer

Alternativas

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

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README del creador

Abrir el README

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.

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

Solo se muestra el principio; lee el resto en el repo

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