MCP is a standard connection layer for AI.
MCP stands for Model Context Protocol. It gives AI applications a consistent way to connect to external systems instead of forcing every AI tool, database, SaaS product, and ERP platform to build a different custom integration.
A simple analogy is USB-C for AI context. The AI application still needs a model. The business system still owns the data. MCP defines a standard way for the two sides to communicate.
The core pieces: host, client, and server.
- MCP host: the AI application a user interacts with, such as Claude, ChatGPT, Cursor, or another MCP-compatible interface.
- MCP client: the connector inside the host that manages a connection to one specific MCP server.
- MCP server: the program or hosted service that exposes data and capabilities from a business system.
For ERP, the MCP server is the important business boundary. It determines what the AI can see, what actions it can request, how permissions are enforced, and whether the answers are based on raw ERP records or a trustworthy data layer.
What MCP servers expose.
MCP servers commonly expose three kinds of capabilities:
- Tools: functions the AI can call, such as querying open orders or looking up inventory.
- Resources: data the AI can read, such as governed records, documents, or analytical datasets.
- Prompts: reusable instructions or workflows that help structure a business question.
In an ERP setting, those capabilities need to be designed around business semantics and security. A CFO does not want a raw table dump. They want the trusted definition of revenue, margin, backlog, inventory, customer, supplier, and item, filtered to the datasets, attributes, and rows that user is allowed to access.
Why ERP MCP is different from generic MCP.
Connecting an AI tool to a file system is not the same as connecting it to ERP data. ERP data carries financial, operational, and security consequences. It is full of duplicates, inconsistent part numbers, multi-entity calendars, historical mappings, and permissions.
The MCP interface does not clean or govern the data by itself. If the ERP layer underneath is fragmented, the AI will inherit that fragmentation. If access is too broad, the AI can become another uncontrolled export path. That is why Marquis IQ treats MCP as an access layer on top of certified connectors, data mastering, analytics models, row-level security, column-level security, and stored interaction history.
What good ERP MCP governance looks like.
For enterprise data, governance has to be managed in the data platform, not left to each AI prompt. Marquis IQ administrators can manage which datasets, which attributes, and which rows of data are accessible by any user. Those proprietary row and column level controls apply across Marquis IQ platforms, including MCP.
Marquis IQ MCP interactions are also stored for transparency and compliance requirements. Users can work in the AI tools they prefer while the enterprise keeps oversight of what data was accessed and how it was used.