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    Vectra AI RUX MCP Server

    mcpother
    io.docker.mcp/vectra-ai-rux-mcp-server · vdf7e65b3e424
    deploy just this

    Vectra AI MCP Server - An MCP server that connects AI assistants & agents to the Vectra AI security platform, enabling intelligent threat analysis and automated incident response.

    run it

    on your own machines, one way per deploy method
    generated from the record, not tested yet
    CHANGE_ME: check the image's README for its transport and port; this assumes streamable HTTP at :8000/mcp. If it speaks stdio only, use it from clients/mcp.json as a `docker run -i` command instead.
    #!/usr/bin/env bash
# vectra-ai-rux (io.docker.mcp/vectra-ai-rux-mcp-server), recipe docker (generated).
# Generated by Hub for toolset "my-agent"; started by mcp-my-agent-vectra-ai-rux.service.
set -euo pipefail
export HOST=127.0.0.1 PORT=8101
exec docker run --rm --name mcp-my-agent-vectra-ai-rux -p 127.0.0.1:${PORT}:8000 -e VECTRA_CLIENT_SECRET mcp/vectra-ai-rux-mcp-server

[Unit]
Description=MCP server vectra-ai-rux (toolset my-agent), generated by Hub
After=network-online.target docker.service
Wants=network-online.target

[Service]
Type=simple
User=mcp
Group=mcp
SupplementaryGroups=docker
EnvironmentFile=/etc/mcp/my-agent.env
Environment=HOME=/var/lib/mcp/vectra-ai-rux
WorkingDirectory=/var/lib/mcp/vectra-ai-rux
ExecStart=/opt/mcp/my-agent/bin/vectra-ai-rux.sh
Restart=on-failure
RestartSec=5
NoNewPrivileges=true
ProtectSystem=strict
ProtectHome=true
PrivateTmp=true
ReadWritePaths=/var/lib/mcp/vectra-ai-rux

[Install]
WantedBy=multi-user.target
    generated from the record, not tested yet
    CHANGE_ME: check the image's README for its transport and port; this assumes streamable HTTP at :8000/mcp. If it speaks stdio only, use it from clients/mcp.json as a `docker run -i` command instead.
    docker run --rm -p 127.0.0.1:8101:8000 -e VECTRA_CLIENT_SECRET mcp/vectra-ai-rux-mcp-server
    generated from the record, not tested yet
    CHANGE_ME: check the image's README for its transport and port; this assumes streamable HTTP at :8000/mcp. If it speaks stdio only, use it from clients/mcp.json as a `docker run -i` command instead.
    # Generated by Hub for toolset "my-agent" (2026-10-19T03:59:47+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:
  vectra-ai-rux:
    image: mcp/vectra-ai-rux-mcp-server
    ports:
    - 127.0.0.1:8101:8000
    restart: unless-stopped
    x-note: 'CHANGE_ME: check the image''s README for its transport and port; this assumes streamable
      HTTP at :8000/mcp. If it speaks stdio only, use it from clients/mcp.json as a `docker run -i` command
      instead.'
    environment:
      VECTRA_CLIENT_SECRET: ${VECTRA_CLIENT_SECRET}
volumes:
  npm-cache: {}
  uv-cache: {}
    generated from the record, not tested yet
    CHANGE_ME: check the image's README for its transport and port; this assumes streamable HTTP at :8000/mcp. If it speaks stdio only, use it from clients/mcp.json as a `docker run -i` command instead.
    # Generated by Hub for toolset "my-agent" (2026-10-19T03:59:47+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: vectra-ai-rux
  namespace: mcp
  labels: &id001
    app: vectra-ai-rux
spec:
  replicas: 1
  selector:
    matchLabels: *id001
  template:
    metadata:
      labels: *id001
    spec:
      containers:
      - name: vectra-ai-rux
        image: mcp/vectra-ai-rux-mcp-server
        ports:
        - containerPort: 8000
          name: http
        env:
        - name: VECTRA_CLIENT_SECRET
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: VECTRA_CLIENT_SECRET
---
apiVersion: v1
kind: Service
metadata:
  name: vectra-ai-rux
  namespace: mcp
spec:
  selector:
    app: vectra-ai-rux
  ports:
  - port: 8000
    targetPort: http
    name: http
    generated from the record, not tested yet
    CHANGE_ME: check the image's README for its transport and port; this assumes streamable HTTP at :8000/mcp. If it speaks stdio only, use it from clients/mcp.json as a `docker run -i` command instead.
    # Generated by Hub for toolset "my-agent" (2026-10-19T03:59:47+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:
  vectra-ai-rux:
    enabled: true
    replicas: 1
    port: 8000
    path: /mcp
    container:
      name: vectra-ai-rux
      image: mcp/vectra-ai-rux-mcp-server
      ports:
      - containerPort: 8000
        name: http
      env:
      - name: VECTRA_CLIENT_SECRET
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: VECTRA_CLIENT_SECRET

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

    in the cloud

    one click: the provider's deploy form opens filled in, in your own account
    koyeb
    deploy to koyeb

    The form opens with CHANGE_ME for each value you supply: VECTRA_CLIENT_SECRET. Fill them in before you deploy.

    Clients connect to the service's https://…koyeb.app address plus /mcp.

    azure
    deploy to azuretemplate

    A Container Apps environment with one app per server, each with HTTPS ingress and one replica. The portal asks for each value you supply as a secure parameter; the deployment's outputs list the endpoints.

    connect to it

    copy what your client needs
    docker run -i --rm -e VECTRA_CLIENT_SECRET mcp/vectra-ai-rux-mcp-server
    {
  "mcpServers": {
    "vectra-ai-rux": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "VECTRA_CLIENT_SECRET",
        "mcp/vectra-ai-rux-mcp-server"
      ],
      "env": {
        "VECTRA_CLIENT_SECRET": "${VECTRA_CLIENT_SECRET}"
      }
    }
  }
}
    #!/usr/bin/env bash
# Registers every server with Claude Code (run once; `claude mcp list` to check).
set -euo pipefail
set -a; [ -f .env ] && source .env; set +a
claude mcp add vectra-ai-rux -e "VECTRA_CLIENT_SECRET=${VECTRA_CLIENT_SECRET}" -- docker run -i --rm -e VECTRA_CLIENT_SECRET mcp/vectra-ai-rux-mcp-server

    {
  "mcpServers": {
    "vectra-ai-rux": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "VECTRA_CLIENT_SECRET",
        "mcp/vectra-ai-rux-mcp-server"
      ],
      "env": {
        "VECTRA_CLIENT_SECRET": "${VECTRA_CLIENT_SECRET}"
      }
    }
  }
}
    {
  "servers": {
    "vectra-ai-rux": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "VECTRA_CLIENT_SECRET",
        "mcp/vectra-ai-rux-mcp-server"
      ],
      "env": {
        "VECTRA_CLIENT_SECRET": "${VECTRA_CLIENT_SECRET}"
      }
    }
  }
}
    name: my-agent
target: local
servers:
- hub: io.docker.mcp/vectra-ai-rux-mcp-server
    • not probed yet: tools unpinned, ingest lists them

    tools

    Not probed yetThe tools appear once the hub or its worker has listed this entry. Running it still works: your client lists them on first start.