Red Hat OpenShift AI (RHOAI) Observability MCP

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