Inline ARM64 guidance in EmbeddingServer step

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: aponcedeleonch

docs/toolhive/tutorials/mcp-optimizer.mdx

@@ -89,16 +89,6 @@ Before starting this tutorial, make sure you have:
 - An MCP client (Visual Studio Code with GitHub Copilot is used in this
   tutorial)
 
-:::tip[ARM64 support]
-
-The default TEI image is x86_64-only. If you are running on ARM64 nodes (for
-example, Apple Silicon with kind), set the `image` field in your EmbeddingServer
-to use the ARM64 image. See
-[EmbeddingServer resource](../guides-vmcp/optimizer.mdx#embeddingserver-resource)
-for details.
-
-:::
-
 ## Step 1: Create an MCPGroup and deploy backend MCP servers
 
 Create an MCPGroup to organize the backend MCP servers that the optimizer will
@@ -195,15 +185,18 @@ The optimizer uses semantic search to find relevant tools. This requires an
 EmbeddingServer, which runs a text embeddings inference (TEI) server.
 
 Create an EmbeddingServer with default settings. This deploys the
-`BAAI/bge-small-en-v1.5` model:
+`BAAI/bge-small-en-v1.5` model. If you are running on ARM64 nodes (for example,
+Apple Silicon with kind), uncomment the `image` line to use the ARM64 build:
 
 ```yaml title="embedding-server.yaml"
 apiVersion: toolhive.stacklok.dev/v1alpha1
 kind: EmbeddingServer
 metadata:
   name: optimizer-embedding
   namespace: toolhive-system
-spec: {}
+spec:
+  # Uncomment for Apple Silicon or other ARM64 platforms
+  # image: ghcr.io/huggingface/text-embeddings-inference:cpu-arm64-latest
 ```
 
 Apply the resource: