> For the complete documentation index, see [llms.txt](https://docs.revenium.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.revenium.io/integrations/mcp-server/rancher-prime.md).

# MCP Server for Rancher Prime

The Revenium MCP Server for Rancher gives Liz — SUSE Rancher Prime's AI assistant — direct access to your Revenium account. Once connected, Liz can answer AI cost questions, set spend alerts, and investigate anomalies as a native member of her agent crew, alongside your Kubernetes and infrastructure tooling.

This repo wraps the standard Revenium MCP server with an HTTP transport so it can run inside a Rancher-managed cluster and be consumed by the Rancher AI Agent — which expects an HTTP endpoint, not the stdio interface used by local clients like Claude Code.

### What Liz Can Do With the Revenium MCP Server

Once connected, Liz can:

* Investigate cost spikes — ask "Why did my AI costs spike yesterday?" and get a breakdown by provider, model, customer, agent, or API key
* Detect anomalies — find abnormal spend patterns across all dimensions, with configurable sensitivity and dollar thresholds
* Set budget alerts — configure Slack or email notifications when monthly, daily, or per-transaction spend exceeds a threshold
* Monitor trends — get cost summaries for any time window (last hour, day, week, or month) broken down any way you need
* Catch cost increases early — set relative-change alerts that fire when spend rises or falls by a percentage week-over-week or month-over-month
* Meter its own usage — track Liz's own API calls back to your Revenium account for full operational transparency

### Requirements

* A Kubernetes cluster managed by Rancher (tested with Rancher 2.13.4)
* Rancher with the AI Assistant feature enabled
* kubectl configured against that cluster

A Revenium API key — get one at [app.revenium.ai](https://app.revenium.ai).

### Getting Started

#### Step 1: Create the API Key Secret

Do this once, outside the YAML, so your key is never committed to version control:

```zsh
kubectl -n cattle-ai-agent-system create secret generic revenium-mcp-credentials \
  --from-literal=REVENIUM_API_KEY=rev_sk_your_api_key_here
```

The MCP server requires a write-scope key (`rev_sk_*`) — it reads account data and can create alerts and modify resources, so a metering-only key (`rev_mk_*`) will be rejected. See [API Key Permissions](/integrations/api-key-permissions.md).

#### Step 2: Deploy the MCP Server

Apply the Rancher deployment manifest from the maintained Revenium MCP Rancher package:

```zsh
kubectl apply -f revenium-mcp-k8s.yaml
```

This deploys the MCP server as a Kubernetes Deployment + Service inside cattle-ai-agent-system. No custom image or registry is required — the container pulls the published `revenium-mcp` package at start.

#### Step 3: Verify the Server is Up

```
kubectl -n cattle-ai-agent-system rollout status deploy/revenium-mcp-server
kubectl -n cattle-ai-agent-system get svc revenium-mcp-server
```

The in-cluster endpoint is:

```
http://revenium-mcp-server.cattle-ai-agent-system.svc
```

{% hint style="info" %}
**Regulated environments.** The default manifest exposes the endpoint as HTTP because in-cluster traffic is typically isolated by the cluster network policy. If your environment requires encryption-in-transit for in-cluster traffic, route the service through your cluster's existing service mesh or ingress TLS termination.
{% endhint %}

#### Step 4: Register the Agent

Apply the AIAgentConfig CR to register the MCP endpoint with the Rancher AI Assistant:

```zsh
kubectl apply -f revenium-agent.yaml
```

This uses the same API the Rancher AI Assistant uses internally to seed its built-in Rancher, Fleet, and Cluster Provisioning agents.

#### Step 5: Confirm Liz Can See It

```
kubectl get aiagentconfigs -n cattle-ai-agent-system -w
```

Once the revenium entry appears, refresh the Rancher AI Assistant. The Revenium agent will show up in the agent picker alongside the built-in agents.\ <br>

<br>


---

# Agent Instructions
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