mcp-server

docker run --rm -p 127.0.0.1:8101:8000 -e VECTROS_API_KEY -e VECTROS_KEYRING_ALIAS -e VECTROS_API_BASE_URL -e VECTROS_MCP_TOOLS node:22-alpine sh -c 'npx -y supergateway@4 --stdio '"'"'npx -y @vectros-ai/[email protected]'"'"' --outputTransport streamableHttp --stateful --host 0.0.0.0 --port 8000 --streamableHttpPath /mcp --healthEndpoint /healthz'

# Generated by Hub for toolset "my-agent" (2026-10-17T22:38:12+00:00).
# Each package server runs behind a stdio->HTTP bridge and listens on localhost only.
#   cp .env.example .env    # fill the ${VAR}s
#   docker compose up -d && bash smoke.sh
# Remote servers are not here: clients reach them directly (see clients/).
name: mcp-my-agent
services:
  vectros-ai:
    image: node:22-alpine
    command:
    - sh
    - -c
    - npx -y supergateway@4 --stdio 'npx -y @vectros-ai/[email protected]' --outputTransport streamableHttp
      --stateful --host 0.0.0.0 --port 8000 --streamableHttpPath /mcp --healthEndpoint /healthz
    ports:
    - 127.0.0.1:8101:8000
    volumes:
    - npm-cache:/root/.npm
    healthcheck:
      test:
      - CMD-SHELL
      - wget -qO- http://127.0.0.1:8000/healthz || exit 1
      interval: 30s
      timeout: 5s
      retries: 5
      start_period: 2m
    restart: unless-stopped
    environment:
      VECTROS_API_KEY: ${VECTROS_API_KEY}
      VECTROS_KEYRING_ALIAS: ${VECTROS_KEYRING_ALIAS}
      VECTROS_API_BASE_URL: ${VECTROS_API_BASE_URL}
      VECTROS_MCP_TOOLS: ${VECTROS_MCP_TOOLS}
volumes:
  npm-cache: {}
  uv-cache: {}

# Generated by Hub for toolset "my-agent" (2026-10-17T22:38:12+00:00).
#   kubectl create namespace mcp
#   kubectl -n mcp create secret generic mcp-credentials --from-env-file=.env
#   kubectl apply -f kubernetes.yaml
# Services are cluster-internal: <name>.mcp.svc.cluster.local:8000/mcp. Put an ingress
# with auth in front before exposing any of them.
apiVersion: v1
kind: Namespace
metadata:
  name: mcp
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vectros-ai
  namespace: mcp
  labels: &id001
    app: vectros-ai
spec:
  replicas: 1
  selector:
    matchLabels: *id001
  template:
    metadata:
      labels: *id001
    spec:
      containers:
      - name: vectros-ai
        image: node:22-alpine
        ports:
        - containerPort: 8000
          name: http
        env:
        - name: VECTROS_API_KEY
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: VECTROS_API_KEY
        - name: VECTROS_KEYRING_ALIAS
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: VECTROS_KEYRING_ALIAS
        - name: VECTROS_API_BASE_URL
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: VECTROS_API_BASE_URL
        - name: VECTROS_MCP_TOOLS
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: VECTROS_MCP_TOOLS
        command:
        - sh
        - -c
        - npx -y supergateway@4 --stdio 'npx -y @vectros-ai/[email protected]' --outputTransport streamableHttp
          --stateful --host 0.0.0.0 --port 8000 --streamableHttpPath /mcp --healthEndpoint /healthz
        readinessProbe:
          httpGet:
            path: /healthz
            port: http
          initialDelaySeconds: 10
---
apiVersion: v1
kind: Service
metadata:
  name: vectros-ai
  namespace: mcp
spec:
  selector:
    app: vectros-ai
  ports:
  - port: 8000
    targetPort: http
    name: http

# Generated by Hub for toolset "my-agent" (2026-10-17T22:38:12+00:00).
# Credentials come from the Secret 'mcp-credentials' (kubectl create secret generic ... --from-env-file=.env).
# Each server under `servers` is one Deployment + Service; the keys after it configure
# upstream charts a worker verified.
servers:
  vectros-ai:
    enabled: true
    replicas: 1
    port: 8000
    path: /mcp
    container:
      name: vectros-ai
      image: node:22-alpine
      ports:
      - containerPort: 8000
        name: http
      env:
      - name: VECTROS_API_KEY
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: VECTROS_API_KEY
      - name: VECTROS_KEYRING_ALIAS
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: VECTROS_KEYRING_ALIAS
      - name: VECTROS_API_BASE_URL
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: VECTROS_API_BASE_URL
      - name: VECTROS_MCP_TOOLS
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: VECTROS_MCP_TOOLS
      command:
      - sh
      - -c
      - npx -y supergateway@4 --stdio 'npx -y @vectros-ai/[email protected]' --outputTransport streamableHttp
        --stateful --host 0.0.0.0 --port 8000 --streamableHttpPath /mcp --healthEndpoint /healthz
      readinessProbe:
        httpGet:
          path: /healthz
          port: http
        initialDelaySeconds: 10

# helm install my-agent helm/my-agent -n mcp