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

Scouter MCP

de 335versión 0.3.5i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.335/scouter-mcp

Instalar

Resumen

Descripción del creador (original en inglés)

Query Scouter APM (objects, counters, XLog transactions) over stdio via a Scouter collector.

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: Otros

Instalación

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

Antes de instalar

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Descargar el archivo .mcpb del creador

El enlace lleva al archivo del propio creador; no lo alojamos ni lo reempaquetamos. Abre el archivo y Claude Desktop lo instalará.

Ejecuta esto en tu terminal

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.

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 6 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub 335, 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 Otrosi

  • Más estrellas que el 0% de los otros 32461 servidores de esta categoría
  • Publicado en el registro más recientemente que el 16% de los otros 32461 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 32461 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 Scouter 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 (0.3.5) llegó al registro hace 4 meses.

¿Qué apps pueden usarlo?

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

¿Qué puedo usar en su lugar?

En la misma categoría: Spec Driven Development, Remote Desktop Commander, dotnet-mcp

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

Spec Driven Development

de formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Se ejecuta en tu computadora★ 440MITActualizado hace 14 meses
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Remote Desktop Commander

de desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Se ejecuta en la nube★ 211Actualizado ayer
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dotnet-mcp

de Skymly

MCP server for .NET symbols, XAML, source generators, and restricted workspace edit

Se ejecuta en tu computadoraActualizado hoy
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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.

scouter-mcp

한국어 문서는 README.ko.md 를 참고하세요.

A stdio MCP server that connects directly to a Scouter Collector over TCP and queries XLogs, counters, and objects. Its purpose is to let an AI quickly explore Scouter metrics and diagnose root causes. Each result carries txid / gxid / objName / endTimeIso, which you can use as keys to cross-analyze with other observability tools such as OpenSearch or Datadog.

Architecture

Java 17. It reuses scouter-common and ports the scouter.webapp net/server classes into the scouter.mcp.client package. MCP uses the Java SDK 2.0.0 stdio transport. All operations against the Collector are read-only.

Build

./gradlew shadowJar

output: build/libs/scouter-mcp- -all.jar

The .mcpb bundle is produced only by the release CI (which wraps this jar); local builds just produce the jar.

Registration (Claude Code, Claude Desktop, ...)

For a single collector, install the .mcpb bundle from the GitHub Release for a one-click setup — or copy .mcp.json.example, point it at the fat jar (downloaded from the release or built locally), and fill in the credentials. For multiple collectors, see Multiple collectors.

Env varDescription
SCOUTER_COLLECTOR_HOSTCollector host
SCOUTER_COLLECTOR_PORTCollector TCP port (default 6100)
SCOUTER_USERLogin user
SCOUTER_PASSWORDLogin password
SCOUTER_TZTime zone (e.g. Asia/Seoul)
SCOUTER_LOCALEUser-facing message locale: en or ko. If unset, derived from the JVM default (Korean only when the JVM language is Korean, otherwise English)
SCOUTER_INCLUDE_BIND_PARAMSOperator kill-switch for SQL bind parameters in get_xlog_detail (default true). Set to false to strip bind params server-side regardless of the per-call argument — an LLM cannot re-enable them. Use when bind values may contain PII.
Multiple collectors

The official release ships one .mcpb bundle (one-click install, a single collector) plus the standalone fat jar. A .mcpb defines exactly one server with one credential set, so it cannot register two collectors at once. For multiple collectors — which usually differ in their whole connection set (host/port and user/password) — use the jar directly and add one entry per collector:

  1. Download scouter-mcp- -all.jar from the GitHub Release.
  2. Add one mcpServers entry per collector to your client config (.mcp.json / claude_desktop_config.json), all pointing at that same jar, each with its own env set. This keeps each credential isolated:
{

"mcpServers": { "scouter-prod": { "command": "java", "args": ["-jar", "/ABSOLUTE/PATH/scouter-mcp- -all.jar"], "env": { "SCOUTER_COLLECTOR_HOST": "prod-collector", "SCOUTER_COLLECTOR_PORT": "6100", "SCOUTER_USER": "prod-user", "SCOUTER_PASSWORD": "*", "SCOUTER_TZ": "Asia/Seoul" } }, "scouter-stg": { "command": "java", "args": ["-jar", "/ABSOLUTE/PATH/scouter-mcp- -all.jar"], "env": { "SCOUTER_COLLECTOR_HOST": "stg-collector", "SCOUTER_COLLECTOR_PORT": "6100", "SCOUTER_USER": "stg-user", "SCOUTER_PASSWORD": "*", "SCOUTER_TZ": "Asia/Seoul" } } } }

The AI then orchestrates across the collectors.

Tools (14)

NamePurposeKey inputs
list_objectsList objects/agentsobjType?, nameLike? (case-insensitive)
search_xlogSearch XLogs (latency/errors)from, to, objNameLike?, objHash?, service?, login?, ip?, desc?, minElapsedMs?, onlyError?, limit? (default 20, max 200)
get_service_summaryPer-service aggregate (count/avg/max/p95/errorRate), top 50from, to, same filters as search_xlog
get_summaryCollector's daily pre-aggregated stats (top-50 SQL/service/error/... — no scanning)category (service/sql/apiCall/ip/userAgent/error/alert), from, to (up to 31 days), objType?, objNameLike?, objHash?
get_xlog_detailXLog detail (SQL/bind params)txid, date?/at?, includeBindParams? (default true)
get_xlog_by_gxidDistributed-transaction groupgxid, date?/at?
get_counterCounter time series (same-day, full resolution)objNameLike\objHashes\objType, counter, from, to
get_counter_statLong-range counter stats (5-min resolution, up to 31 days)objNameLike\objHashes\objType, counter, from, to
list_countersAvailable counters for an objTypeobjType
list_alertsPast collector alertsfrom, to, level?, object?, key?, limit?
get_active_servicesServices running right nowobjNameLike\objType\objHash
list_threadsJVM thread list (state histogram + top 50 by cpu)objNameLike\objHash (max 5 alive instances)
get_thread_detailLive thread of an ACTIVE transaction (stack/lock owner/current SQL)txid (required, active), id?, objNameLike\objHash
get_object_envAgent JVM system properties (secrets masked)objNameLike\objHash, keyLike?
Fuzzy targeting (objNameLike)

Users say app-name fragments ("shop-order-api"), but real objNames embed the k8s pod name (/shop-order-api-deployment-5f4b8c7d9-abcde/shop-order-api1), so objHash changes on every deploy and an app spans multiple instances. objNameLike solves this: a case-insensitive fragment is resolved to all matching instances (alive first, capped at 20) and queried across them — no objHash needed, ever. For XLog search/summary the resolution also unions the collector's daily object DB, so pods replaced by a deploy during the queried window are still found. If nothing matches, the error is NOT_FOUND with a candidates hint listing actual objNames so the caller can self-correct in one step.

Sloppy service queries

Scouter service names look like /api/order/.../search-order-info-grade , but users type "GET orderDetail" or "order info grade". The service filter normalizes such input: an HTTP method is extracted from any position (GET x, x POST, pasted ), whitespace-separated words fall back to the longest token server-side, and explicit * patterns pass through untouched. Server-side matching is still case-sensitive — so when a pattern matches nothing, the same window is re-scanned (bounded) without the service filter and real service names matching the query tokens case-insensitively are returned as serviceCandidates, ordered by traffic. One retry with an exact name resolves it.

service/login/ip/desc use substring match by default (server-side StrMatch), so a short token like search-order-info-grade matches /api/order/ext/order-info/search-order-info-grade . objNameLike/login/ip/desc count as server-side filters, so they relax the 5-minute unfiltered-window cap. list_counters also accepts objNameLike and derives the objType, so users never need to know Scouter's type taxonomy.

All tools are advertised with readOnlyHint. A diagnose_root_cause MCP prompt exposes the recommended tool order for latency/error investigations.

Resource / token safety policy

Production Scouter can produce hundreds of thousands of XLogs in five minutes, so search_xlog enforces guardrails (see scouter.mcp.policy.Limits):

  • During streaming, it stops once the limit or the scan cap (5,000 examined packs) is reached and closes the socket, which also stops the Collector's scan/transfer — bounding server load, network, and MCP heap together.
  • Without a service or objHash filter, only windows up to 5 minutes are allowed; the absolute window cap is 24 hours.
  • limit defaults to 20 and is capped at 200. Results include truncated/scanCapReached and a hint so the caller can narrow filters instead of refetching.
  • get_service_summary retains no rows (only per-service counters), so it uses a higher scan cap (200,000) to cover wider windows cheaply; it reports scanCapReached/examined too.
  • get_counter caps the per-objType fan-out at 20 instances, and downsamples long series with a min/max scheme that preserves spikes/dips (summary min/max/avg are computed from the full series).
  • Windows crossing midnight are split per calendar day (the collector partitions XLogs/counters/alerts by day), so no data is lost on either side of the boundary.
  • Response text budgets: SQL text is cut at 1,500 chars, error messages at 500, thread stack traces at 4,000, env values at 500 — each with a truncation marker carrying the original length. get_xlog_detail profile steps are capped at 150, signalled via totalSteps/stepsTruncated.
  • A single request may fan out to at most 40 collector round-trips (instances x day segments). When client-side filters (minElapsedMs/onlyError) discard over 99% of scanned rows, a low-selectivity hint steers the model toward server-side filters or get_summary.
  • get_summary/get_counter_stat read the collector's daily pre-aggregated data (no scanning), capped at 31 days; summary returns the top 50 rows per category. list_threads caps at 5 alive instances and 50 thread rows each (the state histogram always covers all threads).
  • Per-request telemetry (passes/examined/kept/tookMs) is logged to stderr as structured key=value lines for post-hoc load analysis.

Internationalization

Only dynamic, user-facing output (tool error messages, result hints, notes) is localized, in English and Korean, via messages.properties / messages_ko.properties. Static schema/tool descriptions and structured stderr logs (key=value) remain English for a stable contract and log parsing.

Security notes

  • Read-only against the Collector (no write commands are exposed).
  • Credentials are injected via environment variables only (never in files or arguments as plaintext). Prefer a least-privilege / read-only Scouter account.
  • Transport is plaintext TCP (the Scouter protocol has no TLS): the SHA-256 password digest, the session token, and all XLog/counter data cross the wire unencrypted. Run only inside a trusted network, or tunnel over SSH/VPN. Do not expose the collector port over the public internet.
  • get_xlog_detail bind parameters can contain PII. Set SCOUTER_INCLUDE_BIND_PARAMS=false to strip them server-side (the LLM cannot re-enable them). See the env table above. get_thread_detail's live bind values (SQLActiveBindVar) obey the same kill-switch.
  • get_object_env unconditionally masks values of keys matching password/secret/token/credential/ private — a server-side policy the LLM cannot opt out of.
  • stdout is reserved for JSON-RPC, so all logs go to stderr only.

License / Notice

The scouter.mcp.client package is ported from Scouter v2.20.0 client code (Apache License 2.0). See NOTICE for details.

Known limitations

  1. search_xlog/get_service_summary minElapsedMs/onlyError/limit are applied client-side because the Collector has no native parameters for them. truncated=true is a heuristic (returned count == limit) and can be a false positive.
  2. On a session-expiry (INVALID_SESSION) the client re-logs in once and retries the request; a second failure surfaces as SCOUTER_AUTH_FAILED (no infinite retry loop). The upstream 2-second time-delta refresh daemon is still not ported, so long-running processes may drift slightly for real-time relative queries. Absolute-epoch (historical) queries are unaffected.
  3. list_alerts/get_active_services were ported from the upstream protocol and validated against a collector via the smoke tests (SmokeIT); field coverage may vary by collector version.
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