An MCP server that standardizes and contextualizes Modbus data, enabling AI agents to interact with industrial IoT systems via a uniform tool interface.
https://github.com/kukapay/modbus-mcpStop writing custom Modbus integrations every time you need AI agents to interact with industrial equipment. This MCP server gives your AI agents direct access to PLCs, sensors, and other Modbus devices through a clean, standardized interface.
You're building automation solutions that need AI agents to monitor production lines, adjust setpoints, or analyze equipment data. But there's always that painful integration layer where you're writing yet another Modbus client, handling connection management, parsing register values, and building custom APIs.
Every project becomes a repeat of the same integration work: "How do we get the AI to talk to the PLC?" This MCP server eliminates that entire layer.
Direct AI-to-PLC Communication: Your AI agents can read temperature sensors, control valve positions, or monitor motor speeds using natural language commands like "read the temperature from register 100" or "set the pressure setpoint to 50 PSI."
Universal Modbus Support: Works with TCP, UDP, and serial connections. Whether you're connecting to a modern Ethernet-enabled PLC or an older RS485 device, the interface stays consistent.
Zero Custom Integration Code: No more writing Modbus clients or building REST APIs around industrial protocols. The MCP server handles all the low-level communication details.
Context-Aware Data: Raw register values get contextualized for AI consumption - your agents understand what the data represents, not just raw integers.
Predictive Maintenance: AI agents monitor vibration sensors, temperature readings, and current draw across multiple machines, identifying patterns that indicate impending failures.
# Your AI agent can now do this:
"Check the bearing temperatures on machines 1-5 and alert if any are above 75°C"
Process Optimization: Agents adjust setpoints across multiple control loops based on production targets and efficiency metrics.
# Instead of hardcoded logic:
"Optimize the reactor temperature and pressure to maximize yield while staying within safety limits"
Quality Control Integration: Connect vision systems and measurement devices so AI agents can correlate quality metrics with process parameters in real-time.
Energy Management: Agents monitor power consumption across facilities and automatically adjust non-critical systems during peak demand periods.
The server connects to any Modbus device via environment configuration - no code changes needed for different installations:
# Clone and install
git clone https://github.com/kukapay/modbus-mcp.git
cd modbus-mcp
uv sync
# Configure for your PLC
echo "MODBUS_TYPE=tcp
MODBUS_HOST=192.168.1.100
MODBUS_PORT=502
MODBUS_SLAVE_ID=1" > .env
# Add to Claude Desktop
{
"mcpServers": {
"Modbus": {
"command": "uv",
"args": ["--directory", "/path/to/modbus-mcp", "run", "modbus-mcp"],
"env": {"MODBUS_TYPE": "tcp", "MODBUS_HOST": "192.168.1.100"}
}
}
}
Industrial automation has been isolated from modern AI workflows because of protocol complexity. This server makes industrial data as accessible to AI agents as any web API.
Instead of spending days on integration work for each new industrial AI project, you configure connection parameters and start building intelligence. Your agents can now participate directly in industrial processes, making decisions based on real equipment data rather than working with stale exports or manual data entry.
The standardized MCP interface means you write your AI logic once and it works across different facilities, equipment vendors, and connection types. No more vendor-specific integration code.
Get Started: modbus-mcp on GitHub