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  3. Observability AIops
Observability AIops logo
Health: ActiveRecent health check succeeded.Last checked 9/22/2026, 10:31:37 AM

Observability AIops

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
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Governed Prometheus + Grafana ops: PromQL, alerts, dashboards, RCA; 30 tools.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet β€” we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself β€” its README, its docs, or a verified owner. We haven’t found those for Observability AIops, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing AlternativesπŸ“Š More in Monitoring

Documentation Overview

Observability AIops

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 β€” Prometheus (HTTP API, PromQL, targets, rules, alerts), Alertmanager (alerts + silences), Grafana (dashboards, datasources, folders), and Grafana Loki (bounded LogQL log reads + log RCA) β€” 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 β€” 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.

What it does

Answers the questions an SRE actually repeats over a Prometheus/Grafana stack, and guards the writes that follow:

  • PromQL + metadata β€” instant and range queries, label-value enumeration, and series metadata, all read-only and result-capped.
  • Scrape-target & rule health β€” which targets are up/down (and why, from lastError), which were dropped by relabeling, and which recording/alerting rules are erroring.
  • Alerts & silences β€” firing/pending Prometheus rule alerts, Alertmanager's post-routing view, and its silences.
  • Grafana β€” dashboards, datasources (+ health), and folders.
  • Loki logs β€” bounded LogQL reads (label + label-value enumeration, a validation-gated 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.
  • Flagship analyses β€” transparent heuristics that show their numbers: firing_alert_rca (join each firing alert to its rule expr β†’ cause + action), target_scrape_health_analysis (rank down/erroring scrapes β†’ likely cause), alert_noise_and_flap_analysis (frequently-repeated / duplicate alerts β†’ dedup/rollup recommendation), plus two log analyses β€” log_error_burst_rca (per-stream error burst vs baseline β†’ new-signature / volume-spike / single-instance) and log_volume_analysis (top streams + high-cardinality label warnings + retention hint) β€” and alert_log_context, which correlates a firing Prometheus alert to its Loki streams.
  • Governed writes β€” create/expire Alertmanager silences (time-boxed), create Grafana annotations, update/delete dashboards, and hot-reload the Prometheus config β€” each audited, risk-tiered, dry_run-able, and the reversible ones capture the real fetched before-state for undo.

What this tool does, and does not, decide

It delivers Prometheus + Grafana operations β€” reads and writes β€” 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 β€” 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) β€” so a reviewer can see at a glance that a row was a high-risk delete. It is a label, not a gate.

Capability matrix (39 MCP tools)

GroupPlatformToolsCountR/W
MetricsPrometheusinstant_query, range_query, label_values, series_metadata4read
TargetsPrometheuslist_targets, target_scrape_health, dropped_targets3read
StatusPrometheusprometheus_config_status, prometheus_tsdb_status2read
RulesPrometheuslist_rules, rule_health2read
AlertsPrometheus/Alertmanagerfiring_alerts, pending_alerts, alertmanager_alerts, list_silences4read
GrafanaGrafanalist_dashboards, get_dashboard, list_datasources, datasource_health, list_folders5read
LokiLokiloki_labels, loki_label_values, loki_query, loki_tail_errors4read
Overviewallobservability_overview1read
AnalysesPrometheusfiring_alert_rca, target_scrape_health_analysis, alert_noise_and_flap_analysis3read
Log analysesLokilog_error_burst_rca, log_volume_analysis2read
Cross-signalPrometheus + Lokialert_log_context1read
WritesAlertmanagercreate_silence, expire_silence2write (med)
Grafanacreate_annotation1write (medium)
Grafanaupdate_dashboard1write (med)
Grafanadelete_dashboard1write (high)
Prometheusreload_prometheus_config1write (med)
Undoallundo_list1read
allundo_apply1write (med)

Loki is read-only β€” 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.

Quick start

As a Claude Code plugin

One install gives an agent both the skill and the MCP server:

Code
/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 β€” see below.

As an OpenClaw plugin

The same bundle is published on ClawHub, where one install delivers the skill and its MCP server together:

bash
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 β€” without it the skill still installs but reports Visible to model: no. Credentials are configured exactly as below.

As a CLI or standalone MCP server

bash
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):

server.ts
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.

Governance

Every MCP tool passes through the bundled @governed_tool harness:

  • Audit β€” every call (params, result, status, duration, risk tier, and any operator-supplied approver/rationale) is logged to ~/.observability-aiops/audit.db (relocatable via OBSERVABILITY_AIOPS_HOME). The CLI writes the same row the MCP path does β€” there is no unaudited entry point.
  • Runaway guard β€” a safety backstop, not an authorization gate: the same call hammered in a tight loop trips a circuit breaker. Disable with OBSERVABILITY_RUNAWAY_MAX=0; optional hard ceilings via OBSERVABILITY_MAX_TOOL_CALLS / OBSERVABILITY_MAX_TOOL_SECONDS.
  • Undo recording β€” reversible writes record an inverse descriptor built from the fetched before-state (create_silenceβ†’expire, update_dashboard/delete_dashboardβ†’restore the captured prior model).
  • Risk tier β€” a descriptive label on the audit row derived from risk_level; it gates nothing.

Supported scope & limitations

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

Last commit
7d ago
Most recent push to the default branch.
Directory activity
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Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Observability AIops

We don't have a confirmed install command for Observability AIops yet, so we don't publish a generated one β€” a guessed package name would point at the wrong package or none at all. Follow the project's own README or setup instructions (https://github.com/AIops-tools/Observability-AIops) for the current steps.

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Technical Specs & Signals

CategoryπŸ“ŠMonitoring
More technical detailsExpand β–Ύ
Last updatedSep 16, 2026
8/10 checks healthy over the last 45d
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Last commit7d ago
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Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools11/30
Adoption & activity4/15
Community engagement0/10

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