Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Governed Prometheus + Grafana ops: PromQL, alerts, dashboards, RCA; 39 tools.
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": {
"observability-aiops": {
"command": "uvx",
"args": [
"observability-aiops"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add observability-aiops -- uvx observability-aiopsPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"observability-aiops": {
"command": "uvx",
"args": [
"observability-aiops"
]
}
}
}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": {
"observability-aiops": {
"type": "stdio",
"command": "uvx",
"args": [
"observability-aiops"
]
}
}
}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 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub AIops-tools, 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 (0.13.0) llegó al registro hace 7 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Recta
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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Cheaper cross-chain stablecoin routes for AI agents; 5 % of the verified saving, nothing if none.
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.
Audit availability: Real writes require a committed
pendingaudit row and stop before contacting the platform if storage is unavailable. Reads and previews may continue with a warning; new audit rows store outcome summaries rather than full responses.
Disclaimer: Community-maintained open-source project. Not affiliated with, endorsed by, or sponsored by the Prometheus or Grafana projects, Grafana Labs, or the Cloud Native Computing Foundation. Prometheus, Alertmanager and Grafana are trademarks of their respective owners. MIT licensed.
Governed AI-ops for a self-hosted observability stack in one server — Prometheus (HTTP API, PromQL, targets, rules, alerts), Alertmanager (alerts + silences), Grafana (dashboards, datasources, folders), and Grafana Loki (bounded LogQL log reads + log RCA) — with a built-in governance harness: unified audit log, token/runaway budget guard, undo-token recording, and descriptive risk-tier labels. One config can span your whole stack; each target names its own platform. Beyond the mock test suite, the Prometheus/Alertmanager/Grafana reads, the RCAs, and the governed silence + dashboard write paths (with undo) have been exercised against a live Prometheus 3.x + Alertmanager + Grafana 13 stack — see docs/VERIFICATION.md.
This is the self-hosted-observability complement to enterprise monitoring suites: it speaks the open Prometheus/Grafana APIs an SRE actually runs, not a vendor NMS.
Answers the questions an SRE actually repeats over a Prometheus/Grafana stack, and guards the writes that follow:
lastError), which were dropped by relabeling, and which recording/alerting rules are erroring.query_range, and a canned error-tail), all read-only with a hard lookback + line cap, optional multi-tenant X-Scope-OrgID, and basic/bearer auth per target.firing_alert_rca (join each firing alert to its rule expr → cause + action), target_scrape_health_analysis (rank down/erroring scrapes → likely cause), alert_noise_and_flap_analysis (frequently-repeated / duplicate alerts → dedup/rollup recommendation), plus two log analyses — log_error_burst_rca (per-stream error burst vs baseline → new-signature / volume-spike / single-instance / no-baseline) and log_volume_analysis (top streams + high-cardinality label warnings + retention hint) — and alert_log_context, which correlates a firing Prometheus alert to its Loki streams.dry_run-able, and the reversible ones capture the real fetched before-state for undo.It delivers Prometheus + Grafana operations — reads and writes — accurately and efficiently, and records every one of them. It does not decide whether a write is allowed to happen. That is the agent's judgement, or the permission of the account you connect it with: give it a Grafana token with only Viewer scope, and a Prometheus/Alertmanager reached without the admin/write API, and the writes fail at the server — the place that actually owns the permission.
So there is no read-only switch, no policy file, no approval gate to configure. The one thing the tool guarantees is that nothing is silent: every call, over MCP and over the CLI alike, lands an audit row in ~/.observability-aiops/audit.db, and destructive writes still capture their before-state and record an inverse where one exists.
Each tool declares a
risk_level, carried into the audit row as a descriptive tier
(none/confirm/review) — so a reviewer can see at a glance that a row was a high-risk delete. It
is a label, not a gate.
| Group | Platform | Tools | Count | R/W |
|---|---|---|---|---|
| Metrics | Prometheus | instant_query, range_query, label_values, series_metadata | 4 | read |
| Targets | Prometheus | list_targets, target_scrape_health, dropped_targets | 3 | read |
| Status | Prometheus | prometheus_config_status, prometheus_tsdb_status | 2 | read |
| Rules | Prometheus | list_rules, rule_health | 2 | read |
| Alerts | Prometheus/Alertmanager | firing_alerts, pending_alerts, alertmanager_alerts, list_silences | 4 | read |
| Grafana | Grafana | list_dashboards, get_dashboard, list_datasources, datasource_health, list_folders | 5 | read |
| Loki | Loki | loki_labels, loki_label_values, loki_query, loki_tail_errors | 4 | read |
| Overview | all | observability_overview | 1 | read |
| Analyses | Prometheus | firing_alert_rca, target_scrape_health_analysis, alert_noise_and_flap_analysis | 3 | read |
| Log analyses | Loki | log_error_burst_rca, log_volume_analysis | 2 | read |
| Cross-signal | Prometheus + Loki | alert_log_context | 1 | read |
| Writes | Alertmanager | create_silence, expire_silence | 2 | write (med) |
| Grafana | create_annotation | 1 | write (medium) | |
| Grafana | update_dashboard | 1 | write (med) | |
| Grafana | delete_dashboard | 1 | write (high) | |
| Prometheus | reload_prometheus_config | 1 | write (med) | |
| Undo | all | undo_list | 1 | read |
| all | undo_apply | 1 | write (med) |
Loki is read-only — Loki exposes no safe operational write surface (no silence/annotation analogue), so this tool deliberately ships no Loki writes.
The CLI exposes a convenience subset (query, logs, alert, overview, …); the full 39-tool surface is via the MCP server.
One install gives an agent both the skill and the MCP server:
/plugin marketplace add AIops-tools/marketplace
/plugin install observability-aiops@aiops-tools
The MCP server is fetched with uv and pinned to the package version this plugin declares, so an audit row can be traced back to the code that wrote it. Credentials are still configured with observability-aiops init — see below.
The same bundle is published on ClawHub, where one install delivers the skill and its MCP server together:
openclaw plugins install clawhub:@zw008/observability-aiops
openclaw skills info observability-aiops # expect: Visible to model: yes
Restart the OpenClaw gateway afterwards so it loads the plugin. The MCP server is fetched with uv, pinned to this exact release, so uvx has to be on PATH — without it the skill still installs but reports Visible to model: no. Credentials are configured exactly as below.
uv tool install observability-aiops # or: pipx install observability-aiops
observability-aiops init # wizard: pick platform (prometheus/grafana) + store the token (encrypted) observability-aiops doctor # verify config, secrets, connectivity observability-aiops overview # snapshot: firing alerts + targets up/down + rules erroring observability-aiops query instant 'up' # run a PromQL instant query observability-aiops logs errors '{app="api"}' # tail error-level Loki logs (bounded) observability-aiops alert rca # root-cause the firing alerts
Run as an MCP server (stdio):
export OBSERVABILITY_AIOPS_MASTER_PASSWORD=... # unlock secrets non-interactively
observability-aiops mcp
Where that password then lives: an exported variable is readable by
every process this shell starts and is recorded by shell history. On a
shared or long-lived host, prefer the interactive prompt, or inject it from
a secret manager for the life of the one command that needs it.
Every MCP tool passes through the bundled @governed_tool harness:
~/.observability-aiops/audit.db (relocatable via OBSERVABILITY_AIOPS_HOME). The CLI writes the same row the MCP path does — there is no unaudited entry point.OBSERVABILITY_RUNAWAY_MAX=0; optional hard ceilings via OBSERVABILITY_MAX_TOOL_CALLS / OBSERVABILITY_MAX_TOOL_SECONDS.create_silence→expire, update_dashboard/delete_dashboard→restore the captured prior model).risk_level; it gates nothing.docker run prom/prometheus, grafana/grafana, grafana/loki), so observability-aiops doctor is the fastest live check (Prometheus /api/v1/status/buildinfo, Grafana /api/health, Loki /ready + /loki/api/v1/status/buildinfo). See docs/VERIFICATION.md.Want another read, an analysis tuned, or a platform capability that isn't here? Open an issue or a PR — feedback and contributions are welcome.