MCP and AI context: How MCP connects agents to enterprise knowledge

The Model Context Protocol, or MCP, is quickly becoming part of the enterprise AI conversation.

Its appeal is easy to understand. Instead of building a different custom integration every time an AI assistant or agent needs to interact with an external system, MCP provides a standardized way for AI applications to connect to tools, data sources, and workflows. The official MCP documentation describes it as an open standard for connecting AI applications to external systems.

But connecting AI to an enterprise system is only one part of the problem.

An AI agent may be able to access a catalog, warehouse, or business application through MCP and still lack the information it needs to use that system correctly. It may find the right table but misunderstand the metric. It may retrieve a definition without knowing that it has been deprecated. It may identify several similar datasets without knowing which one the business considers authoritative.

This is where the distinction between MCP and AI context becomes important.

MCP can help deliver context. It does not automatically create that context.

For a broader introduction, start with our guide to what a context layer is.

MCP and AI context at a glance

	MCP	Context layer
Primary role	Connect AI applications to external systems	Provide the enterprise knowledge AI needs to interpret information correctly
What it provides	Standardized access to tools, resources, and capabilities	Meaning, relationships, trust, provenance, governance, and other relevant knowledge
Main question	How can AI access this system?	What does AI need to know before using this information?
Typical components	MCP clients, servers, tools, resources	Catalog metadata, glossary terms, lineage, ownership, quality, policies, semantics
Creates business meaning?	No	Yes, by connecting to governed sources of enterprise knowledge
Can they work together?	Yes	Yes

The most useful architecture is not MCP or a context layer. It is MCP connected to a strong source of enterprise context.

What is MCP?

The Model Context Protocol is an open standard that allows AI applications to connect to external systems through a common interface.

Instead of every AI application requiring a separate bespoke integration with every external tool, an MCP-compatible client can connect to MCP servers that expose specific resources and capabilities.

This can include:

• Enterprise applications
• Data sources
• Search systems
• Developer tools
• Knowledge repositories
• Business workflows
• Metadata platforms
• Data catalogs

The value is standardization.

For developers, that can reduce some of the integration work involved in making external capabilities available to AI. For AI applications, it creates a consistent way to interact with a growing ecosystem of connected systems.

But the quality of what an AI system receives through MCP still depends on the quality of the underlying source.

Why MCP does not solve the context problem by itself

Imagine an AI assistant can connect to your data ecosystem through MCP.

A user asks:

“Which revenue dataset should I use for the board report?”

The connection works perfectly. The AI can search available assets.

It finds three datasets:

• revenue_final
• revenue_reporting_v2
• finance_revenue_global

Technically, MCP has done its job. The assistant successfully connected to the external system and retrieved information.

But which dataset should the AI recommend?

To answer that question reliably, it needs more than connectivity. It may need to know:

• Which dataset is certified
• Which team owns each asset
• Whether any source has been deprecated
• Which definition of revenue each dataset uses
• Whether the data is current
• Which source Finance uses for board reporting
• Whether the user has permission to access it

That knowledge is enterprise context.

MCP can provide a path to that information, but something still needs to create, structure, govern, and maintain it.

Connectivity vs. context

AI needs to know…	MCP can provide access?	Requires governed context?
Which systems can I interact with?	✓
How do I call a tool or retrieve a resource?	✓
What does “revenue” mean in this company?		✓
Which dataset is trusted?		✓
Who owns this information?		✓
Where did the data come from?		✓
Is the source current and certified?		✓
Which policy applies?		✓
Can this user access it?	MCP can carry the request	✓
Which information is relevant to this task?	Partly	✓

This is why organizations evaluating MCP should avoid treating protocol adoption as equivalent to AI readiness.

The connection matters, but what sits behind the connection matters more.

Where does enterprise AI context come from?

Much of the context AI needs already exists inside the organization.

It may come from a data catalog, a semantic layer, a business glossary, governance systems, data quality platforms, lineage tools, documentation, or other knowledge sources.

For example, a modern data catalog can provide:

• Business definitions
• Technical metadata
• Ownership
• Certifications
• Data lineage
• Relationships
• Policies
• Data quality signals
• Usage information
• Documentation

A semantic layer may add consistent metrics and calculation logic, while governance systems add policies and permissions.

The context layer brings this knowledge together.

MCP can then provide one way for AI applications to access it.

For a deeper breakdown of the roles played by each technology, see context layer vs. data catalog vs. semantic layer.

How MCP fits into a context layer architecture

A simplified enterprise architecture can be represented as:

Enterprise systems
↓
Governed enterprise knowledge
↓
Context layer
↓
MCP / APIs / retrieval
↓
AI assistants and agents

The important point is the order.

The organization first needs meaningful, governed knowledge. That context can then be exposed through MCP or other delivery mechanisms.

Reversing the logic creates a common problem: organizations build strong AI connectivity before deciding what information the AI should actually trust.

That can produce assistants with excellent access to poorly maintained context.

What can an MCP server expose to AI?

An MCP server can expose different types of resources and capabilities depending on the system behind it.

For a data catalog, useful context might include:

• Search results for data assets
• Business definitions
• Object descriptions
• Ownership
• Tags and classifications
• Relationships
• Hierarchies
• Data lineage
• Documentation
• Comments
• Governance information

The DataGalaxy MCP Server exposes governed DataGalaxy catalog, lineage, and business glossary context to compatible AI agents. DataGalaxy states that the server exposes metadata rather than underlying enterprise data and inherits DataGalaxy roles, domains, and access controls.

That distinction is important.

The goal is not necessarily to send raw enterprise data through the context connection. It can be to give AI the knowledge about the data it needs before selecting, explaining, or working with it.

How a data catalog becomes useful to AI through MCP

Traditionally, a person might open a data catalog and search manually.

An analyst could look up a KPI definition. A data engineer might trace lineage. A business user might identify the owner of a dashboard.

MCP allows compatible AI applications to bring some of that catalog knowledge into the environments where users are already asking questions.

For example, instead of manually opening the catalog to investigate a table, a developer working with an AI assistant could potentially ask:

“Is this table still approved for use?”

or:

“What downstream assets depend on this column?”

or:

“What does this field mean according to the business glossary?”

The AI can query the catalog rather than trying to infer the answer from a schema or codebase.

This is one of the reasons DataGalaxy positions the Catalog as a shared knowledge foundation for both people and AI. The platform connects metadata, lineage, ownership, business meaning, and governance, which can then provide richer context to AI workflows.

MCP for data engineers

For technical teams, one of the most immediate uses of MCP is reducing context switching.

A data engineer or developer working inside an AI-enabled development environment may need to understand:

• What a table represents
• Whether it is still maintained
• Which fields contain sensitive information
• Who owns it
• What depends on it
• Whether a schema change will create downstream issues

Without enterprise context, an AI coding assistant may understand the code but know very little about the meaning of the organization’s data.

Connecting the assistant to governed metadata can help bridge that gap.

The AI is no longer working only from the code currently visible in the prompt. It can retrieve additional knowledge about the data environment when relevant.

MCP for business users

The same principle applies to less technical users.

A business user should not need to understand schemas, catalog structures, or lineage diagrams to benefit from enterprise data knowledge.

They might simply ask:

“What does active customer mean?”

“Which dashboard should I use for monthly recurring revenue?”

“Who owns this KPI?”

“Where does this number come from?”

An AI assistant connected to governed enterprise context can translate those natural-language questions into searches against the organization’s existing knowledge.

This is where MCP becomes part of a broader “talk to your data” experience.

The user sees a conversation.

Behind it, the AI is using governed context to understand the organization’s language, assets, relationships, and rules.

MCP for AI agents

The stakes become higher when AI moves from answering questions to taking actions.

An autonomous or semi-autonomous agent may need to decide which dataset to use, whether an asset is appropriate, which dependencies could be affected by a change, or whether a specific action is permitted.

In those situations, context becomes part of the control system.

Before acting, an agent may need information about:

• Ownership
• Data classifications
• Policies
• Dependencies
• Quality
• Provenance
• Certification
• Business definitions

MCP can provide standardized access to these sources, but the organization still needs strong governance around what the server exposes and what the agent is allowed to do with that information.

The more autonomy the agent has, the less acceptable it becomes to rely on undocumented assumptions.

What makes enterprise MCP trustworthy?

MCP connectivity alone is not enough for enterprise deployments. Organizations should evaluate the knowledge and controls behind the server.

1. Governed source information

The server should connect AI to sources of context that teams actually maintain and trust.

2. Permissions

The AI should not receive context the requesting user or service is not authorized to access.

3. Traceability

It should be possible to understand which enterprise source supported the AI’s answer or action.

4. Freshness

Definitions, lineage, ownership, and other context need to stay current as the data estate changes.

5. Business meaning

Technical metadata alone is rarely enough. AI also needs definitions, terminology, and business relationships.

6. Limited exposure

Organizations should understand exactly what information an MCP server makes available and whether it exposes metadata, raw data, actions, or a combination of these.

For a wider evaluation framework, see how to evaluate context layer tools.

MCP is not the only way to deliver context

MCP is receiving significant attention, but it should not be treated as the only possible integration mechanism.

Enterprise context may also be delivered through:

• APIs
• Search
• RAG pipelines
• Native integrations
• Agent frameworks
• Application-specific connectors

Different AI use cases may require different patterns.

The reason MCP is strategically interesting is not that it eliminates every other architecture. It is that it provides a common interface through which compatible AI applications can connect to external capabilities.

That can make enterprise context more portable across assistants and agent environments.

The underlying principle remains the same: the organization should manage trusted knowledge independently of the AI interface consuming it.

MCP vs. RAG for enterprise context

MCP and RAG are often mentioned in the same conversation, but they solve different problems.

	MCP	RAG
Primary role	Connect AI applications to external systems and capabilities	Retrieve relevant information and add it to model context
Typical use	Tools, resources, workflows, live system interaction	Documents, knowledge bases, search results
Standardized protocol?	Yes	No single universal protocol
Can access live capabilities?	Yes	Usually focused on retrieval
Creates business context?	No	No
Can use governed enterprise context?	Yes	Yes

An organization might use both.

For example, an AI agent could connect to a metadata platform through MCP while also using RAG to retrieve supporting documentation.

The important question is not which acronym wins.

It is whether the AI receives accurate, relevant, governed context for the decision it is making.

Questions to ask before connecting MCP to enterprise data

Before rolling out an MCP server, teams should be able to answer several questions:

• What information will the server expose?
• Is it metadata, raw data, actions, or all three?
• Where does the business context come from?
• Who owns and maintains that context?
• How are user permissions enforced?
• How quickly do changes propagate?
• Can AI answers be traced back to the source?
• Can the same context be reused across different AI clients?
• What happens when definitions conflict?
• How are deprecated or low-quality assets represented?

These questions move the conversation from “Do we support MCP?” to “Will the AI receive context it can actually trust?”

That is a much more useful enterprise evaluation.

How DataGalaxy approaches MCP and AI context

DataGalaxy starts with the governed knowledge inside the DataGalaxy Catalog.

The Catalog brings together technical metadata, ownership, definitions, lineage, governance, and trust information. Its Business Glossary adds shared business terminology, while lineage helps connect data with its origins and dependencies.

The DataGalaxy MCP Server then provides a standardized way for compatible AI applications to access this governed context. According to DataGalaxy, it can expose catalog assets, object information, classifications, relationships, hierarchy, tasks, comments, and other metadata while inheriting platform access controls.

This separates two responsibilities:

DataGalaxy Catalog manages the knowledge.

The MCP Server makes that knowledge available to AI.

That distinction is central to building AI systems that do more than connect to enterprise data. They need to understand the context around it.

Frequently asked questions

What is MCP in AI?

MCP, or Model Context Protocol, is an open standard for connecting AI applications to external systems such as tools, data sources, and workflows.

Is MCP the same as a context layer?

No. MCP is a connection protocol. A context layer provides the business meaning, relationships, trust signals, provenance, and governance an AI system needs to interpret enterprise information correctly.

Does MCP provide context?

MCP can provide access to sources of context, but the quality of the context depends on the external system being connected. An MCP server connected to a governed data catalog can expose much richer context than one connected only to raw schemas.

What is an MCP server?

An MCP server exposes capabilities, tools, or resources from an external system to MCP-compatible AI applications.

Can MCP connect AI to a data catalog?

Yes. A catalog can expose metadata and governed enterprise knowledge through an MCP server. DataGalaxy provides an MCP Server for this purpose.

Does the DataGalaxy MCP Server expose enterprise data?

DataGalaxy states that its MCP Server exposes metadata such as glossary terms, lineage, documentation, and ownership according to platform permissions rather than exposing the underlying enterprise data itself.

Is MCP better than RAG?

They are not direct substitutes. RAG is primarily a retrieval pattern, while MCP standardizes connections between AI applications and external systems. An enterprise AI architecture can use both.

Connection is only useful when the context behind it is trustworthy

MCP can make it significantly easier for AI applications to interact with enterprise systems.

But better connectivity does not remove the need for good data knowledge.

If AI is going to use your metadata, definitions, lineage, ownership, and governance to answer questions or take actions, that knowledge needs to be accurate, current, and trusted.

The opportunity is therefore bigger than connecting AI to more systems.

It is connecting AI to the context that helps it understand those systems correctly.

Explore the DataGalaxy MCP Server or learn more about the DataGalaxy Catalog.

About the author: Max Faivre

Product Marketing Manager

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