x=hub

your toolset

    build deployment
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    mcp

    mcpkey · package
    io.github.florentine-ai/mcp · v0.2.1
    deploy just this

    MCP server for Florentine.ai - Natural language to MongoDB aggregations

    run it

    on your own machines, one way per deploy method
    generated from the record, not tested yet
    #!/usr/bin/env bash
# florentine-ai (io.github.florentine-ai/mcp), recipe baremetal (generated).
# Generated by Hub for toolset "my-agent"; started by mcp-my-agent-florentine-ai.service.
set -euo pipefail
export HOST=127.0.0.1 PORT=8101
exec npx -y supergateway@4 --stdio 'npx -y @florentine-ai/[email protected]' --outputTransport streamableHttp --stateful --host 127.0.0.1 --port 8101 --streamableHttpPath /mcp --healthEndpoint /healthz

[Unit]
Description=MCP server florentine-ai (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/florentine-ai
WorkingDirectory=/var/lib/mcp/florentine-ai
ExecStart=/opt/mcp/my-agent/bin/florentine-ai.sh
Restart=on-failure
RestartSec=5
NoNewPrivileges=true
ProtectSystem=strict
ProtectHome=true
PrivateTmp=true
ReadWritePaths=/var/lib/mcp/florentine-ai

[Install]
WantedBy=multi-user.target
    generated from the record, not tested yet
    docker run --rm -p 127.0.0.1:8101:8000 -e FLORENTINE_TOKEN -e LLM_SERVICE -e LLM_KEY -e SESSION_ID -e RETURN_TYPES -e REQUIRED_INPUTS node:22-alpine sh -c 'npx -y supergateway@4 --stdio '"'"'npx -y @florentine-ai/[email protected]'"'"' --outputTransport streamableHttp --stateful --host 0.0.0.0 --port 8000 --streamableHttpPath /mcp --healthEndpoint /healthz'
    generated from the record, not tested yet
    # Generated by Hub for toolset "my-agent" (2026-10-18T06:33:38+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:
  florentine-ai:
    image: node:22-alpine
    command:
    - sh
    - -c
    - npx -y supergateway@4 --stdio 'npx -y @florentine-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:
      FLORENTINE_TOKEN: ${FLORENTINE_TOKEN}
      LLM_SERVICE: ${LLM_SERVICE}
      LLM_KEY: ${LLM_KEY}
      SESSION_ID: ${SESSION_ID}
      RETURN_TYPES: ${RETURN_TYPES}
      REQUIRED_INPUTS: ${REQUIRED_INPUTS}
volumes:
  npm-cache: {}
  uv-cache: {}
    generated from the record, not tested yet
    # Generated by Hub for toolset "my-agent" (2026-10-18T06:33:38+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: florentine-ai
  namespace: mcp
  labels: &id001
    app: florentine-ai
spec:
  replicas: 1
  selector:
    matchLabels: *id001
  template:
    metadata:
      labels: *id001
    spec:
      containers:
      - name: florentine-ai
        image: node:22-alpine
        ports:
        - containerPort: 8000
          name: http
        env:
        - name: FLORENTINE_TOKEN
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: FLORENTINE_TOKEN
        - name: LLM_SERVICE
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: LLM_SERVICE
        - name: LLM_KEY
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: LLM_KEY
        - name: SESSION_ID
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: SESSION_ID
        - name: RETURN_TYPES
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: RETURN_TYPES
        - name: REQUIRED_INPUTS
          valueFrom:
            secretKeyRef:
              name: mcp-credentials
              key: REQUIRED_INPUTS
        command:
        - sh
        - -c
        - npx -y supergateway@4 --stdio 'npx -y @florentine-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: florentine-ai
  namespace: mcp
spec:
  selector:
    app: florentine-ai
  ports:
  - port: 8000
    targetPort: http
    name: http
    generated from the record, not tested yet
    # Generated by Hub for toolset "my-agent" (2026-10-18T06:33:38+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:
  florentine-ai:
    enabled: true
    replicas: 1
    port: 8000
    path: /mcp
    container:
      name: florentine-ai
      image: node:22-alpine
      ports:
      - containerPort: 8000
        name: http
      env:
      - name: FLORENTINE_TOKEN
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: FLORENTINE_TOKEN
      - name: LLM_SERVICE
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: LLM_SERVICE
      - name: LLM_KEY
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: LLM_KEY
      - name: SESSION_ID
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: SESSION_ID
      - name: RETURN_TYPES
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: RETURN_TYPES
      - name: REQUIRED_INPUTS
        valueFrom:
          secretKeyRef:
            name: mcp-credentials
            key: REQUIRED_INPUTS
      command:
      - sh
      - -c
      - npx -y supergateway@4 --stdio 'npx -y @florentine-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

    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: FLORENTINE_TOKEN, LLM_SERVICE, LLM_KEY, SESSION_ID, RETURN_TYPES, REQUIRED_INPUTS. 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
    npx -y @florentine-ai/[email protected]
# env: FLORENTINE_TOKEN, LLM_SERVICE, LLM_KEY, SESSION_ID, RETURN_TYPES, REQUIRED_INPUTS
    {
  "mcpServers": {
    "florentine-ai": {
      "command": "npx",
      "args": [
        "-y",
        "@florentine-ai/[email protected]"
      ],
      "env": {
        "FLORENTINE_TOKEN": "${FLORENTINE_TOKEN}",
        "LLM_SERVICE": "${LLM_SERVICE}",
        "LLM_KEY": "${LLM_KEY}",
        "SESSION_ID": "${SESSION_ID}",
        "RETURN_TYPES": "${RETURN_TYPES}",
        "REQUIRED_INPUTS": "${REQUIRED_INPUTS}"
      }
    }
  }
}
    #!/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 florentine-ai -e "FLORENTINE_TOKEN=${FLORENTINE_TOKEN}" -e "LLM_SERVICE=${LLM_SERVICE}" -e "LLM_KEY=${LLM_KEY}" -e "SESSION_ID=${SESSION_ID}" -e "RETURN_TYPES=${RETURN_TYPES}" -e "REQUIRED_INPUTS=${REQUIRED_INPUTS}" -- npx -y @florentine-ai/[email protected]

    {
  "mcpServers": {
    "florentine-ai": {
      "command": "npx",
      "args": [
        "-y",
        "@florentine-ai/[email protected]"
      ],
      "env": {
        "FLORENTINE_TOKEN": "${FLORENTINE_TOKEN}",
        "LLM_SERVICE": "${LLM_SERVICE}",
        "LLM_KEY": "${LLM_KEY}",
        "SESSION_ID": "${SESSION_ID}",
        "RETURN_TYPES": "${RETURN_TYPES}",
        "REQUIRED_INPUTS": "${REQUIRED_INPUTS}"
      }
    }
  }
}
    {
  "servers": {
    "florentine-ai": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@florentine-ai/[email protected]"
      ],
      "env": {
        "FLORENTINE_TOKEN": "${FLORENTINE_TOKEN}",
        "LLM_SERVICE": "${LLM_SERVICE}",
        "LLM_KEY": "${LLM_KEY}",
        "SESSION_ID": "${SESSION_ID}",
        "RETURN_TYPES": "${RETURN_TYPES}",
        "REQUIRED_INPUTS": "${REQUIRED_INPUTS}"
      }
    }
  }
}
    name: my-agent
target: local
servers:
- hub: io.github.florentine-ai/mcp
    • 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.