Which integrations are available for this server?

Provides tools for listing Grafana dashboards and retrieving their panel queries to visualize observability data. Provides tools for querying Kubernetes cluster state, including pods, events, nodes, and KServe InferenceServices. Enables executing PromQL queries against a Prometheus-compatible metrics store to retrieve time-series metrics data. Provides access to Thanos Querier for executing PromQL queries across scalable, long-term metrics storage.

How do I use RHOAI Observability MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@RHOAI Observability MCP check vLLM metrics for model llm-vllm" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

RHOAI Observability MCP

Official

by opendatahub-io

Overview Schema Related Servers Score Discussions

Python

Remote

Red Hat OpenShift AI (RHOAI) Observability MCP

Build codecov Python 3.11+ License: MIT

An MCP (Model Context Protocol) server that gives AI assistants direct access to Red Hat OpenShift AI observability data. Query Prometheus metrics, Alertmanager alerts, Loki logs, Grafana dashboards, and Kubernetes cluster state to troubleshoot vLLM inference workloads.

Features

21 tools across 7 categories for comprehensive observability
vLLM-aware metrics (TTFT, TPOT, E2E latency, KV cache, queue depth)
Composite investigation tools that correlate metrics, logs, and alerts automatically
Auto-detection of in-cluster vs external access to OpenShift services
Built on FastMCP with async backends via httpx

Architecture

graph TD
    A[Claude / AI Assistant] -->|MCP Protocol| B[rhoai-observability-mcp]
    B --> C[Thanos / Prometheus]
    B --> D[Alertmanager]
    B --> E[Loki]
    B --> F[Tempo]
    B --> G[Grafana]
    B --> H[Kubernetes / OpenShift]

Backends:

Backend	Purpose	Source
Prometheus (Thanos)	Metrics queries (PromQL)	`backends/prometheus.py`
Alertmanager	Active alerts and alert groups	`backends/alertmanager.py`
Loki	Log queries (LogQL)	`backends/loki.py`
Tempo	Distributed trace queries (TraceQL)	`backends/tempo.py`
Grafana	Dashboard discovery and panel queries	`backends/grafana.py`
Kubernetes (OpenShift)	Pods, events, nodes, InferenceServices	`backends/openshift.py`

Quick Start

# Clone and install
git clone https://github.com/opendatahub-io/rhoai-observability-mcp.git
cd rhoai-observability-mcp
uv pip install -e ".[dev]"

# Configure (see INSTALL.md for all options)
export THANOS_URL=https://thanos-querier.openshift-monitoring.svc:9091
export ALERTMANAGER_URL=https://alertmanager-main.openshift-monitoring.svc:9093
export OPENSHIFT_TOKEN=$(oc whoami -t)

# Run
python -m rhoai_obs_mcp.server

See INSTALL.md for detailed setup, configuration, and Claude Desktop integration.

Build & Deploy

Build the container image

make build

Override the image name or tag:

make build IMAGE_NAME=quay.io/myorg/rhoai-observability-mcp IMAGE_TAG=v1.0.0

Push to registry

make push

Deploy to OpenShift

Prerequisites: oc login to your cluster, kustomize installed, and create the target project:

oc new-project rhoai-obs-mcp

Then deploy:

make deploy

This uses Kustomize to build the OpenShift overlay (deploy/overlays/openshift/) on top of the base manifests (deploy/base/) and applies them to the rhoai-obs-mcp namespace. To deploy to a different namespace:

make deploy NAMESPACE=my-namespace

Undeploy

make undeploy

If you deployed to a custom namespace, pass the same value:

make undeploy NAMESPACE=my-namespace

CI-built images

Container images are automatically built from main and published to GHCR:

ghcr.io/opendatahub-io/rhoai-observability-mcp:latest

Local Development with Kind

Set up a local Kubernetes cluster with mock observability backends for development and testing:

# Prerequisites: kind, kubectl, helm, kustomize
make kind-up

This creates a Kind cluster, installs Prometheus + Alertmanager + Grafana via Helm, deploys a fake vLLM metrics exporter, and deploys the MCP server. Access the MCP server at http://localhost:30080.

To point at real external backends instead of the mocks:

make kind-deploy THANOS_URL=https://real-cluster:9091 ALERTMANAGER_URL=https://real-cluster:9093 GRAFANA_URL=https://real-cluster:3000 TEMPO_URL=https://real-cluster:8080

Tear down:

make kind-down

Tool Reference

Metrics

Tool	Description
`query_prometheus`	Execute a raw PromQL query against ThanosQuerier
`query_prometheus_range`	Execute a PromQL range query to get time-series data (trends, spikes, correlations)
`get_vllm_metrics`	Get a summary of key vLLM metrics (TTFT, TPOT, E2E, cache, queue) for a model
`list_metrics`	List available Prometheus metric names, optionally filtered by regex

Alerts

Tool	Description
`get_alerts`	Get active alerts from Alertmanager, filterable by severity and labels
`get_alert_groups`	Get alerts grouped by their routing labels

Logs

Tool	Description
`query_logs`	Execute a LogQL query against OpenShift LokiStack
`get_pod_logs`	Get logs for a specific pod by namespace and name

Traces

Tool	Description
`get_trace`	Fetch a distributed trace by its trace ID
`search_traces`	Search for traces using TraceQL expressions
`list_trace_tags`	List available trace tag names for building TraceQL queries

Cluster

Tool	Description
`get_pods`	List pods in a namespace with status, restarts, and creation time
`get_events`	List Kubernetes events, filterable by resource and reason
`get_node_status`	Get node status, capacity, and GPU allocation info
`describe_resource`	Get detailed description of a Kubernetes resource
`get_inference_services`	List KServe InferenceService resources

Dashboards

Tool	Description
`list_dashboards`	List available Grafana dashboards, filterable by tag or title
`get_dashboard_panels`	Get panels and their queries from a Grafana dashboard

Investigation

Tool	Description
`investigate_latency`	Correlate latency metrics, error logs, and alerts for a vLLM model
`investigate_gpu`	Correlate GPU utilization, KV cache, queue depth, and pod status
`investigate_errors`	Correlate error logs, alerts, and Kubernetes events in a namespace

Documentation

INSTALL.md -- Installation, configuration, and integration
TESTING.md -- Running tests and writing new ones
CONTRIBUTING.md -- Development setup and contribution guidelines

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Issues opened vs closed

Resources

GitHub Repository

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