x=hub

your toolset

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    SchemaBrain

    mcpkey · package
    io.github.Arun-kc/schemabrain · v0.6.0
    deploy just this

    The trust and intelligence layer between AI agents and your database.

    run it

    on your own machines, one way per deploy method
    generated from the record, not tested yet
    #!/usr/bin/env bash
# schemabrain (io.github.Arun-kc/schemabrain), recipe baremetal (generated).
# Generated by Hub for toolset "my-agent"; started by mcp-my-agent-schemabrain.service.
set -euo pipefail
export HOST=127.0.0.1 PORT=8101
exec uvx mcp-proxy --host 127.0.0.1 --port 8101 --pass-environment -- uvx schemabrain==0.6.0

[Unit]
Description=MCP server schemabrain (toolset my-agent), generated by Hub
After=network-online.target
Wants=network-online.target

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

[Install]
WantedBy=multi-user.target
    generated from the record, not tested yet
    docker run --rm -p 127.0.0.1:8101:8000 -e DATABASE_URL ghcr.io/astral-sh/uv:python3.12-bookworm-slim sh -c 'uvx mcp-proxy --host 0.0.0.0 --port 8000 --pass-environment -- uvx schemabrain==0.6.0'
    generated from the record, not tested yet
    # Generated by Hub for toolset "my-agent" (2026-10-18T14:14:24+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:
  schemabrain:
    image: ghcr.io/astral-sh/uv:python3.12-bookworm-slim
    command:
    - sh
    - -c
    - uvx mcp-proxy --host 0.0.0.0 --port 8000 --pass-environment -- uvx schemabrain==0.6.0
    ports:
    - 127.0.0.1:8101:8000
    volumes:
    - uv-cache:/root/.cache/uv
    healthcheck:
      test:
      - CMD
      - python3
      - -c
      - import urllib.request; urllib.request.urlopen('http://127.0.0.1:8000/status')
      interval: 30s
      timeout: 5s
      retries: 5
      start_period: 2m
    restart: unless-stopped
    environment:
      DATABASE_URL: ${DATABASE_URL}
volumes:
  npm-cache: {}
  uv-cache: {}
    generated from the record, not tested yet
    # Generated by Hub for toolset "my-agent" (2026-10-18T14:14:24+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: schemabrain
  namespace: mcp
  labels: &id001
    app: schemabrain
spec:
  replicas: 1
  selector:
    matchLabels: *id001
  template:
    metadata:
      labels: *id001
    spec:
      containers:
      - name: schemabrain
        image: ghcr.io/astral-sh/uv:python3.12-bookworm-slim
        ports:
        - containerPort: 8000
          name: http
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: DATABASE_URL
        command:
        - sh
        - -c
        - uvx mcp-proxy --host 0.0.0.0 --port 8000 --pass-environment -- uvx schemabrain==0.6.0
        readinessProbe:
          httpGet:
            path: /status
            port: http
          initialDelaySeconds: 10
---
apiVersion: v1
kind: Service
metadata:
  name: schemabrain
  namespace: mcp
spec:
  selector:
    app: schemabrain
  ports:
  - port: 8000
    targetPort: http
    name: http
    generated from the record, not tested yet
    # Generated by Hub for toolset "my-agent" (2026-10-18T14:14:24+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:
  schemabrain:
    enabled: true
    replicas: 1
    port: 8000
    path: /mcp
    container:
      name: schemabrain
      image: ghcr.io/astral-sh/uv:python3.12-bookworm-slim
      ports:
      - containerPort: 8000
        name: http
      env:
      - name: DATABASE_URL
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: DATABASE_URL
      command:
      - sh
      - -c
      - uvx mcp-proxy --host 0.0.0.0 --port 8000 --pass-environment -- uvx schemabrain==0.6.0
      readinessProbe:
        httpGet:
          path: /status
          port: http
        initialDelaySeconds: 10

# 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: DATABASE_URL. 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
    uvx schemabrain==0.6.0
# env: DATABASE_URL
    {
  "mcpServers": {
    "schemabrain": {
      "command": "uvx",
      "args": [
        "schemabrain==0.6.0"
      ],
      "env": {
        "DATABASE_URL": "${DATABASE_URL}"
      }
    }
  }
}
    #!/usr/bin/env bash
# Registers every server with Claude Code (run once; `claude mcp list` to check).
set -euo pipefail
set -a; [ -f .env ] && source .env; set +a
claude mcp add schemabrain -e "DATABASE_URL=${DATABASE_URL}" -- uvx schemabrain==0.6.0

    {
  "mcpServers": {
    "schemabrain": {
      "command": "uvx",
      "args": [
        "schemabrain==0.6.0"
      ],
      "env": {
        "DATABASE_URL": "${DATABASE_URL}"
      }
    }
  }
}
    {
  "servers": {
    "schemabrain": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "schemabrain==0.6.0"
      ],
      "env": {
        "DATABASE_URL": "${DATABASE_URL}"
      }
    }
  }
}
    name: my-agent
target: local
servers:
- hub: io.github.Arun-kc/schemabrain
    • 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.