Industrial teams lose time navigating dashboards, searching for tags, exporting telemetry, or writing one-off scripts to answer operational questions. Tasks like checking device status, validating signals, reviewing history, or managing edge applications often require switching between multiple tools.
Litmus MCP Server introduces a conversational operational layer over Litmus Edge, allowing teams to query devices, telemetry, historical data, containers, and data models using natural language. By exposing operational capabilities through MCP, it enables AI-assisted workflows that reduce manual effort and accelerate troubleshooting, validation, and automation.
Litmus MCP Server acts as the AI interaction layer for industrial edge operations. While Litmus Edge collects and contextualizes operational data, Litmus MCP Server exposes those capabilities through standardized MCP tools that AI systems, chat interfaces, IDEs, and agents can access.
This architecture enables industrial teams to move from manual edge operations to AI-assisted execution across devices, telemetry, and applications.
standardized industrial data architecture
optimized packing and volume tracking
standardized industrial data architecture
Real-time monitoring and corrective action
Core capabilities that enable conversational interaction with industrial edge environments.
Built-in Web UI
Many MCP implementations require external client setup before teams can validate workflows. Litmus MCP Server includes a built-in browser-based chat interface so teams can launch the server, connect to Litmus Edge, and begin interacting with operational data immediately.
Key features:
Device & Tag Operations
Litmus MCP Server exposes core Litmus Edge device capabilities as MCP tools, allowing AI systems and conversational workflows to interact directly with industrial
Key features:
TELEMETRY & HISTORY OPERATIONS
Operational troubleshooting often requires reviewing both live telemetry and recent history. Litmus MCP Server supports real-time topic inspection and time-series queries so teams can quickly investigate abnormal conditions or missing data.
Key features:
Container Operations
Edge applications increasingly run in containers, yet deployment and monitoring often remain manual tasks. Litmus MCP Server exposes container operations as MCP tools, allowing AI systems and operators to inspect, deploy, and validate applications running on Litmus Edge.
Key features:
DATA MODEL OPERATIONS
AI-assisted operations become more powerful when plant context is structured. Litmus MCP Server exposes data model and asset context operations so AI systems can interact with assets, hierarchies, and attributes instead of raw signals alone.
Key features:
Open MCP Compatibility
Litmus MCP Server follows the Model Context Protocol standard, providing a consistent tool layer that AI systems and developer environments can access. Teams can interact through browser chat, developer tools, or automation agents using the same operational interface.
Key features:
Accelerate proof-of-value with built-in chat interaction
Reduce manual scripting for repetitive operational tasks
Enable AI-assisted troubleshooting and validation
Reuse the same operational tool layer across chat, IDEs, and agents
Support a scalable path toward AI-driven industrial operations
Quick answers to your industrial data questions.
