Glossary
What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is an open standard for connecting AI systems to external data sources, tools, and services. Instead of each AI assistant building its own proprietary connectors, MCP defines a shared interface that any AI tool can use to access any compatible data source.
Think of MCP as USB for AI. Before USB, every device had its own connector. MCP does the same for AI-to-data connections: one standard protocol instead of dozens of custom integrations.
Why MCP exists
AI assistants like Microsoft Copilot, ChatGPT, and Claude are powerful at understanding questions. But understanding is only half the problem. The other half is getting accurate data to answer those questions.
Without a standard protocol, every AI tool needs its own connector to every data source. This creates an explosion of point-to-point integrations, each with its own security model, authentication flow, and data format. MCP replaces this with a single, open interface.
MCP is an emerging standard with growing adoption across the AI ecosystem. As more AI tools and data platforms support MCP, the protocol becomes more valuable for enterprises looking to connect AI to their data.
How MCP works
A standard interface between AI systems and data sources.
Standard interface
MCP defines how AI tools request data and how data sources respond. Both sides speak the same language, regardless of vendor or platform.
Tool discovery
AI systems can discover what data and tools are available through an MCP-compatible source. No hardcoded connections. The AI learns what it can access at connection time.
Vendor-neutral
MCP is an open standard. It is not tied to any specific AI vendor, cloud provider, or database. Any AI tool or data source that implements MCP can connect to any other.
MCP and enterprise data access
MCP solves the connectivity problem: how does an AI tool talk to a data source? But it does not solve the governance problem: should this AI tool have access to this data? What business rules apply? Who audits the access?
This is where platforms built on MCP add value. dhino is MCP-native, meaning it speaks the protocol natively while adding the governance layer enterprises require: templates that carry business logic, access controls that determine who sees what, and audit trails that record every request.
MCP provides: standard connectivity between AI tools and data sources.
dhino adds: governed, template-based execution so AI gets accurate answers without direct database access.
See how dhino Trust uses MCP for governed Copilot access to enterprise data.
See MCP-native data access in action
Learn how dhino uses Model Context Protocol to give AI tools governed access to enterprise data.