AI assistants and agents are only useful when they can reach the right data and tools. Writing a separate integration for every application and every data source does not scale. The Model Context Protocol (MCP), introduced by Anthropic in 2024, is an open protocol that connects AI applications to external tools and data sources in a standard way.
How does MCP work?
- Client: The AI application (for example a chat assistant or agent) connects to MCP servers.
- Server: Exposes a system’s capabilities over MCP.
- Tools: Functions the model can call (for example “fetch the downtime events of the last 24 hours”).
- Resources: Data the model can read as context (documents, tables, files).
- Prompts: Reusable prompt templates.
Once a server is written, different MCP-capable clients can use it the same way; no custom integration is needed for each client–system pair.
Why industry calls for different thinking
An office assistant drafting a wrong email can be corrected; a wrong command on a production line creates safety and quality risk. When using MCP in industrial settings, these questions are decisive:
- What data is accessed? Raw tags, or modeled and contextualized data?
- What actions are possible? Read-only, or writes/commands too?
- Who knows who accessed what, and when? Is there traceability and an audit log?
- How are answers verified? If the model misreads a piece of data, is there a mechanism that catches it?
Principles for industrial MCP
- Read-only first: Start with tools that only read; write/command capabilities are a separate decision and need additional approval.
- Curated tools: Connect the agent not to raw databases or all tags but to purpose-specific, limited and documented tools.
- Contextualized data: The agent should not have to interpret addresses such as “DB12.DBD40”; it should see modeled data with a known unit, asset and quality. (Data modeling)
- Least privilege: Each agent or user should reach only the scope it needs.
- Human approval: Any action that could affect production should have human approval or a separate approval path.
- Logging and traceability: Record each call with who made it, when and with which parameters.
- Network and identity security: MCP endpoints are also an attack surface; authentication, network placement and segmentation rules apply.
Example scenarios
- Downtime analysis: Answering “what was the longest stop on line 2 last night?” with a read-only tool that fetches modeled downtime data from MES/SCADA.
- Maintenance assistant: Combining an asset’s latest readings and maintenance history into a summary for the technician.
- Reporting: Pulling defined metrics (such as OEE) from the semantic layer to prepare an executive summary.
MCP with HighByte Intelligence Hub
HighByte Intelligence Hub supports MCP in both directions: the MCP Server exposes data pipelines as “tools” for AI agents, and the MCP Client connects to third-party MCP servers. The agent thus reaches not raw OT systems but pipelines that have been modeled, conditioned and governed in the platform. This is a practical way to limit agent access to a curated and managed surface. See our Industrial Analytics & AI page and our HighByte page for details.
Where to start
- Pick a single use case with clear value and low risk (for example a read-only downtime summary).
- Model and contextualize the data it needs; do not expose raw tags to the agent.
- Keep tools minimal and documented; define access and logging rules up front.
- Verify outputs with people; widen the scope as confidence grows.
For a broader view of agents working with industrial data, see our AI agents and industrial data article.
