What is the Model Context Protocol (MCP)?

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The Model Context Protocol (MCP) is an open standard designed to facilitate secure and structured communication between AI agents, such as large language models (LLMs), and enterprise data systems. MCP enables agents to access rich metadata and governed datasets in a standardized way, ensuring reliable, auditable, and scalable data interactions. As generative AI becomes increasingly embedded in enterprise workflows, MCP provides a common protocol to ensure that AI tools can query data safely and accurately across platforms, regardless of vendor or architecture.

MCP supports the exchange of metadata, including table definitions, measures, dimensions, hierarchies, descriptions, and user entitlements, providing AI agents with the essential context needed to interpret and query enterprise data.

The Importance of Metadata in AI Systems

Modern AI systems, especially LLMs, require more than access to raw data. To generate reliable outputs, these systems must understand the structure, meaning, and relationships within data. This is where metadata, data about data, becomes essential.

MCP offers a way to expose metadata in a machine-consumable format, enabling LLMs to:

  • Discover available datasets and understand their schema
  • Interpret user questions with greater semantic accuracy
  • Respect governance rules and user permissions
  • Deliver consistent results across tools and departments

As the industry shifts toward AI-native interfaces and agent-based systems, protocols like MCP help bridge the gap between language-driven interfaces and structured enterprise data.

How MCP Works

While implementations may vary, MCP typically involves a service or endpoint that:

  1. Exposes a metadata interface for LLMs and agents to discover data models.
  2. Supports interactive querying via natural language by translating questions into structured queries using the metadata.
  3. Enforces access controls, limiting data exposure based on user roles and enterprise policies.
  4. Returns answers grounded in semantically enriched business data.

MCP’s design parallels that of long-established data protocols like JDBC, but it is optimized for AI workloads and dynamic, conversational query patterns.

Common Use Cases for MCP

  • Enterprise Chatbots: Internal-facing bots that can answer questions grounded in business data.
  • AI Copilots: Tools embedded in productivity apps to help users generate reports, summaries, or analyses.
  • Custom AI Agents: Purpose-built systems for specific business functions like inventory monitoring, sales forecasting, or compliance.
  • BI Assistant Integrations: Enhancing traditional BI platforms with natural language interfaces that respect existing semantic logic.

Benefits of Model Context Protocols

  1. Vendor Neutrality: MCP is an open standard, not tied to any one vendor, allowing diverse systems to adopt a unified approach to AI data access.
  2. Secure and Governed Access: Protocols like MCP ensure that AI tools can access data within the same governance frameworks as BI tools, enforcing role-based access, row-level security, and auditability.
  3. Improved Accuracy for LLMs: With access to metadata, LLMs can better understand schema, definitions, and user context, reducing hallucinations and misinterpretations.
  4. Reduced Redundancy: Instead of duplicating data or building one-off integrations, MCP enables the reuse of existing semantic models and data access policies, thereby reducing redundancy.

Challenges and Considerations

  • Agent Compatibility: Not all LLMs or AI systems currently support MCP. Adoption depends on the AI ecosystem recognizing the need for structured access to metadata.
  • Metadata Quality: For MCP to deliver value, the underlying metadata must be rich, up-to-date, and consistent. Incomplete or outdated definitions can lead to inaccurate results.
  • Governance Integration: Effective use of MCP requires alignment with the organization’s broader data governance strategy.
  • Developer Enablement: Teams require clear documentation and SDKs to build agents that efficiently consume MCP endpoints.

MCP and Semantic Layers

The connection between MCP and semantic layers is foundational. A semantic layer defines business logic, metrics, and relationships in a centralized, governed model. MCP provides the protocol to expose this model to AI systems in a secure and scalable way.

When paired together:

  • The semantic layer defines “what” the data means.
  • MCP defines “how” that meaning is shared with AI tools.

Together, they ensure AI agents operate with the same clarity, consistency, and controls that BI tools have relied on for years.

How AtScale Supports MCP

AtScale offers a robust implementation of MCP within its universal semantic layer platform. By deploying AtScale’s containerized MCP server, enterprises can expose their semantic models to any MCP-compatible AI agent.

Key Features of AtScale’s MCP Implementation:

  • Open Architecture: Connect Claude, ChatGPT, or custom-built agents without building new pipelines.
  • Real-Time Model Discovery: New models become queryable instantly after deployment.
  • Zero-Copy Access: No need to replicate or federate data.
  • Enterprise-Grade Governance: Policies from BI tools extend seamlessly to AI agents.
  • One-to-Many Efficiency: Serve BI tools, AI agents, and analytics apps from a single governed model.

AtScale’s MCP endpoint helps enterprises scale AI safely by offering the metadata context LLMs need, while keeping sensitive data protected.

Why MCP Matters Now

As organizations integrate generative AI into business processes, the need for a structured, secure, and open method of connecting AI agents to governed data becomes clear. MCP provides this foundation, turning the semantic layer into a dynamic, AI-ready interface.

For enterprises looking to future-proof their data architecture while embracing AI, MCP represents a critical evolution. And with vendors like AtScale supporting this protocol, adopting MCP becomes not only possible, but strategic.

Explore how AtScale’s implementation of MCP can unlock AI-native, governed access to your enterprise data.

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