Tecan Cavro Syringe Pump
mcpkey · packageio.github.K-Dense-AI/labmcp-cavro · v0.1.3
MCP server for Tecan Cavro syringe pumps (XLP 6000, XMP 6000, XCalibur) over the DT protocol.
run it
on your own machines, one way per deploy methodgenerated from the record, not tested yet
#!/usr/bin/env bash # labmcp-cavro (io.github.K-Dense-AI/labmcp-cavro), recipe baremetal (generated). # Generated by Hub for toolset "my-agent"; started by mcp-my-agent-labmcp-cavro.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 labmcp-cavro==0.1.3 [Unit] Description=MCP server labmcp-cavro (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/labmcp-cavro WorkingDirectory=/var/lib/mcp/labmcp-cavro ExecStart=/opt/mcp/my-agent/bin/labmcp-cavro.sh Restart=on-failure RestartSec=5 NoNewPrivileges=true ProtectSystem=strict ProtectHome=true PrivateTmp=true ReadWritePaths=/var/lib/mcp/labmcp-cavro [Install] WantedBy=multi-user.target
generated from the record, not tested yet
docker run --rm -p 127.0.0.1:8101:8000 -e LABMCP_ADDRESS -e LABMCP_SIMULATE -e LABMCP_READ_ONLY -e LABMCP_LIMITS ghcr.io/astral-sh/uv:python3.12-bookworm-slim sh -c 'uvx mcp-proxy --host 0.0.0.0 --port 8000 --pass-environment -- uvx labmcp-cavro==0.1.3'
generated from the record, not tested yet
# Generated by Hub for toolset "my-agent" (2026-10-18T11:06:09+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:
labmcp-cavro:
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 labmcp-cavro==0.1.3
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:
LABMCP_ADDRESS: ${LABMCP_ADDRESS}
LABMCP_SIMULATE: ${LABMCP_SIMULATE}
LABMCP_READ_ONLY: ${LABMCP_READ_ONLY}
LABMCP_LIMITS: ${LABMCP_LIMITS}
volumes:
npm-cache: {}
uv-cache: {}
generated from the record, not tested yet
# Generated by Hub for toolset "my-agent" (2026-10-18T11:06:09+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: labmcp-cavro
namespace: mcp
labels: &id001
app: labmcp-cavro
spec:
replicas: 1
selector:
matchLabels: *id001
template:
metadata:
labels: *id001
spec:
containers:
- name: labmcp-cavro
image: ghcr.io/astral-sh/uv:python3.12-bookworm-slim
ports:
- containerPort: 8000
name: http
env:
- name: LABMCP_ADDRESS
valueFrom:
secretKeyRef:
name: mcp-credentials
key: LABMCP_ADDRESS
- name: LABMCP_SIMULATE
valueFrom:
secretKeyRef:
name: mcp-credentials
key: LABMCP_SIMULATE
- name: LABMCP_READ_ONLY
valueFrom:
secretKeyRef:
name: mcp-credentials
key: LABMCP_READ_ONLY
- name: LABMCP_LIMITS
valueFrom:
secretKeyRef:
name: mcp-credentials
key: LABMCP_LIMITS
command:
- sh
- -c
- uvx mcp-proxy --host 0.0.0.0 --port 8000 --pass-environment -- uvx labmcp-cavro==0.1.3
readinessProbe:
httpGet:
path: /status
port: http
initialDelaySeconds: 10
---
apiVersion: v1
kind: Service
metadata:
name: labmcp-cavro
namespace: mcp
spec:
selector:
app: labmcp-cavro
ports:
- port: 8000
targetPort: http
name: http
generated from the record, not tested yet
# Generated by Hub for toolset "my-agent" (2026-10-18T11:06:09+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:
labmcp-cavro:
enabled: true
replicas: 1
port: 8000
path: /mcp
container:
name: labmcp-cavro
image: ghcr.io/astral-sh/uv:python3.12-bookworm-slim
ports:
- containerPort: 8000
name: http
env:
- name: LABMCP_ADDRESS
valueFrom:
secretKeyRef:
name: mcp-credentials
key: LABMCP_ADDRESS
- name: LABMCP_SIMULATE
valueFrom:
secretKeyRef:
name: mcp-credentials
key: LABMCP_SIMULATE
- name: LABMCP_READ_ONLY
valueFrom:
secretKeyRef:
name: mcp-credentials
key: LABMCP_READ_ONLY
- name: LABMCP_LIMITS
valueFrom:
secretKeyRef:
name: mcp-credentials
key: LABMCP_LIMITS
command:
- sh
- -c
- uvx mcp-proxy --host 0.0.0.0 --port 8000 --pass-environment -- uvx labmcp-cavro==0.1.3
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
-
The form opens with CHANGE_ME for each value you supply: LABMCP_ADDRESS, LABMCP_SIMULATE, LABMCP_READ_ONLY, LABMCP_LIMITS. Fill them in before you deploy.
Clients connect to the service's https://…koyeb.app address plus /mcp.
- azure
-
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 needsuvx labmcp-cavro==0.1.3 # env: LABMCP_ADDRESS, LABMCP_SIMULATE, LABMCP_READ_ONLY, LABMCP_LIMITS
{
"mcpServers": {
"labmcp-cavro": {
"command": "uvx",
"args": [
"labmcp-cavro==0.1.3"
],
"env": {
"LABMCP_ADDRESS": "${LABMCP_ADDRESS}",
"LABMCP_SIMULATE": "${LABMCP_SIMULATE}",
"LABMCP_READ_ONLY": "${LABMCP_READ_ONLY}",
"LABMCP_LIMITS": "${LABMCP_LIMITS}"
}
}
}
}
#!/usr/bin/env bash
# Registers every server with Claude Code (run once; `claude mcp list` to check).
set -euo pipefail
set -a; [ -f .env ] && source .env; set +a
claude mcp add labmcp-cavro -e "LABMCP_ADDRESS=${LABMCP_ADDRESS}" -e "LABMCP_SIMULATE=${LABMCP_SIMULATE}" -e "LABMCP_READ_ONLY=${LABMCP_READ_ONLY}" -e "LABMCP_LIMITS=${LABMCP_LIMITS}" -- uvx labmcp-cavro==0.1.3
{
"mcpServers": {
"labmcp-cavro": {
"command": "uvx",
"args": [
"labmcp-cavro==0.1.3"
],
"env": {
"LABMCP_ADDRESS": "${LABMCP_ADDRESS}",
"LABMCP_SIMULATE": "${LABMCP_SIMULATE}",
"LABMCP_READ_ONLY": "${LABMCP_READ_ONLY}",
"LABMCP_LIMITS": "${LABMCP_LIMITS}"
}
}
}
}
{
"servers": {
"labmcp-cavro": {
"type": "stdio",
"command": "uvx",
"args": [
"labmcp-cavro==0.1.3"
],
"env": {
"LABMCP_ADDRESS": "${LABMCP_ADDRESS}",
"LABMCP_SIMULATE": "${LABMCP_SIMULATE}",
"LABMCP_READ_ONLY": "${LABMCP_READ_ONLY}",
"LABMCP_LIMITS": "${LABMCP_LIMITS}"
}
}
}
}
name: my-agent target: local servers: - hub: io.github.K-Dense-AI/labmcp-cavro
- 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.