What is the Model Context Protocol (MCP)?

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.

After launching MCP in November 2024, Anthropic has seen its first wave of traction in the AI ecosystem. Gartner predicts that by 2026, 75% of API gateway vendors and 50% of iPaaS vendors will include MCP in their offerings. OpenAI has also implemented the Protocol in their Agents SDK. Soon after its implementation, the Protocol became a standard for the integration of AI tools, data sources, and other agents. The trend suggests a change in the market in the way organizations incorporate AI technologies, moving away from siloed custom implementations, into a single, coherent, and interoperable system.

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.

Why MCP Matters in Modern 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. And to progress from simply answering questions to executing tasks on their own, AI systems will require constant contextual awareness and uniform access across enterprise systems. This is where metadata, data about data, becomes essential. 

To avoid having AI models spend time on trivial tasks, enterprise systems must be able to seamlessly interconnect.This presents a unique challenge, like how do companies avoid having to do custom code integrations of their own for each of the systems? Before MCP, companies had to spend huge amounts of development time deploying what they called “glue code” to enable their AI systems to work with their data infrastructure. MCP provides a universal standard to enable unprecedented levels of interoperability, thus streamlining the complexity of AI adoption. MCP also 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
• Minimize hallucinations by providing structured, verified responses
  

Studies demonstrate MCP’s impact on reliability and performance. In testing by Twilio, MCP increased task success rates from 92.3% to 100% while speeding up agentic performance by 20% and reducing compute costs by up to 30%. By ensuring AI agents access specific, real-time data through standardized protocols, MCP prevents the generic or fabricated outputs that undermine trust in AI systems.

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 (Architecture and Flow)

MCP follows a client-server architecture with three core components that work together to enable standardized communication between AI systems and enterprise data:

MCP Host

The host is the AI application where the model or agent operates, such as Claude Desktop, an AI-enhanced IDE, or any LLM-powered interface. The host creates and manages multiple client instances, controls connection permissions, enforces security policies, and coordinates context aggregation across different servers.

MCP Client

Each client is created by the host and maintains an isolated, stateful connection with a single MCP server. The client handles protocol negotiation, routes messages between the host and server, manages subscriptions and notifications, and maintains security boundaries to ensure servers cannot access information beyond their scope.

MCP Server

Servers expose specific tools, resources, and data through a defined interface. Each server provides focused functionality in isolation, whether that means accessing a database, executing code, or retrieving files. Multiple servers can be combined seamlessly through the standardized protocol.

The communication flow works as follows:

1. Discovery: The client and server complete an initialization handshake to negotiate protocol compatibility and exchange capabilities. The client queries what tools, resources, and prompts the server offers.
2. Request: When a user makes a request, or the LLM determines it needs external information, the host instructs the client to invoke specific capabilities from the appropriate server.
3. Translation: The client translates the request into the standardized MCP format and routes it to the server.
4. Execution: The server performs the requested task, whether querying a database, executing a function, or retrieving a resource, while enforcing access controls and governance policies.
5. Response: Results are returned in a structured, machine-readable format through the client back to the host, which incorporates them into the LLM’s context or presents them to the user.

This architecture transforms the traditional M×N integration problem (where M AI applications must connect to N tools) into a more manageable M+N problem, where each component implements the MCP standard only once. The design ensures that servers receive only necessary contextual information while the full conversation history and cross-server coordination remain controlled by the host.

MCP vs. RAG vs. Traditional Integrations

When organizations set up AI-enabled systems, they sometimes get mixed up because of the different ways data is accessed. Traditional integration, RAG, and MCP all do different things, and knowing this allows teams to better select the architecture they need. Traditional integrations use custom and bespoke one-off connectors between systems. Each time a new AI application is added to a database, CRM, or business tool, developers need to write new bespoke integrations with endpoints and manual error handling. While this approach works well for simpler and low-volume use cases, over time and as the scenario gets more complex, things get more difficult to maintain.

Retrieval-Augmented Generation (RAG) approaches the problem differently by pulling external data to enrich prompts before generating a response. RAG is best suited for augmented queries with access to static unstructured data such as documents, manuals, and policies. The approach works by having the system keep a set of indexed content, then, for a given query, searches for relevant text fragments and loads them into the prompt as context. While RAG can answer the question “What do we say in the employee handbook about remote work?”, it is more troublesome and difficult when context is needed in the form of real, up-to-date data or when the answer is dependent on some specific user. 

None of these approaches is mutually exclusive. MCP provides a standardized way for models to access tools and live data during generation rather than before it. Instead of pre-loading documents, MCP enables AI agents to query APIs, execute functions, and retrieve real-time information on demand. This makes MCP ideal for transactional queries, dashboard metrics, CRM records, or any scenario where freshness and precision matter. MCP transforms the integration problem from M×N to M+N because each component implements the standard only once.

Common Use Cases for MCP

MCP enables a wide range of enterprise applications by providing consistently available tools and information across multiple business functions. Almost every organization— from Bloomberg to Block — has implemented MCP-powered solutions that minimize deployment times from days to minutes without compromising security and governance.

• 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.
• DevOps Automation: MCP-powered tools help engineering teams refactor legacy software, migrate databases, run unit tests, and automate repetitive coding tasks, accelerating development cycles while maintaining code quality.
• ERP and Financial System Integration: AI agents can securely access enterprise resource planning systems like Dynamics 365 to execute business actions, query financial data, and interact with analytics through governed, standardized protocols that ensure compliance and auditability.

Benefits of Model Context Protocols

MCP provides some of the most important advantages concerning the core issues of your enterprise AI deployment. MCP’s advantages touch on more than just the operational level and even include important enterprise-wide business improvements.

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.
5. Reduced Development Time and Complexity: MCP eliminates the need to create separate, specific integrations for each tool, simplifying development and accelerating the rollout of new features.
6. Consistency Across Teams and Deployments: MCP ensures AI behaves consistently no matter who’s using it or where it’s deployed, which effectively streamlines results organization-wide. 
7. Enhanced Scalability: MCP manages context for thousands of users and inputs without overwhelming development resources or causing system sprawl. Scalability enables organizations to expand AI capabilities without proportional increases in infrastructure complexity.
8. Future-Proofing Enterprise AI: Once a system is exposed through an MCP server, it becomes instantly compatible with any compliant AI application without additional integration work or repeated security reviews.
9. Rapid Multi-Source Integration: MCP enables rapid integration of multiple data sources such as CRM systems, ERP software, marketing analytics, and support platforms without traditional technical friction and long development cycles.

Challenges and Considerations

Despite the major advantages MCP offers, organizations still must address several operational and security challenges when scaling their AI deployments. This includes the peculiar challenges of technical implementation, regulatory alignment, and the new security threats agentic AI poses.

• 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.
• Over-Granting Tool Permissions: MCP servers can request broad permission scopes that create security risks when tokens are over-permissioned, long-lived, and unscoped.
• Poorly Defined Tool Interfaces: MCP servers can modify their tool definitions between sessions, potentially presenting different capabilities than what was initially approved.
• Incorrect Model Assumptions and Misalignment: Misalignment between model expectations and tool output formats can cause AI agents to misinterpret data or execute unintended actions.
• Tool Poisoning and Prompt Injection: MCP is vulnerable to attacks where malicious instructions invisible to humans but understandable to AI agents can trigger automated actions beyond text generation.
• Need for Careful Monitoring and Supply Chain Risks: Organizations must implement continuous monitoring, as MCP servers from community sources or third-party developers can be backdoored or manipulated.

MCP Implementation Considerations and Best Practices 

Successfully deploying MCP requires more than technical integration. Organizations need a structured approach that balances security, governance, and scalability while enabling teams to leverage AI capabilities effectively.

Inventory Data Sources and Tools

Start by mapping which systems should be MCP-enabled based on business value and risk profile. Prioritize high-impact use cases like CRM data, analytics platforms, and operational databases that deliver immediate value while maintaining manageable complexity. This deliberate tool selection results in agents that are faster, more cost-effective, and more reliable than those overwhelmed with unnecessary integrations.

Define Clear Access Boundaries

Governance policies should specify which data sources certain AI applications can use and the conditions under which they can be executed. Use osmotic or server partitions for operational workloads, ensuring there is clear separation of the data and permissions. This logical isolation is the most complex design with the simplest form of multi-tenancy.

Control Permissions and Authentication

Implement OAuth2 for all MCP servers, using short-lived, scoped tokens instead of static API keys. Create dedicated service accounts with minimal permissions that grant access only to what’s necessary for specific tasks. Regular credential rotation and read-only access, where full permissions are not mandatory, significantly reduce security exposure.

Monitor and Log Model-Tool Interactions

Enable comprehensive logging for all MCP activity to create audit trails that track what data was accessed, by whom, and when. Ensure audit logs contain contextual metadata beyond debugging information to provide genuine visibility into system activities. Use an MCP gateway between agents and servers to enhance observability, maintain structured logging, and detect unusual behavior patterns in real time.

Version Control for Tool Definitions

Track changes to MCP server configurations and tool definitions through version control systems to prevent unauthorized modifications. Implement an approval process for new MCP servers and conduct regular scans after installing new tools or updates to detect vulnerabilities. This supply chain security prevents tool poisoning and ensures servers don’t modify their capabilities between sessions without explicit consent.

Test Model Behavior with Real-World Access

Begin with pilot projects focused on low-risk, high-value use cases that allow for learning and validation. Start with read-only integrations before enabling write operations, and test thoroughly in development environments with realistic data and access patterns. Establish clear success metrics for pilot projects and implement gradual rollouts to minimize risk while gathering feedback that informs broader deployment.

MCP and the Future of AI Agents

AI agents have developed from being simple assistants that answer one-off queries to self-contained systems that can autonomously pursue objectives over an extended time horizon. These systems can retain context over multiple actions, maintain session continuity, and collaborate with other agents to achieve objectives that are beyond the capacity of any single agent. MCP offers the standardized infrastructure from which this capability emerged, allowing seamless context retention and transfer across different types of agents.

Multi-agent orchestration represents the next frontier of enterprise AI. Specialized agents can execute parallel processing across different facets of the same task, and MCP manages the calls required to pull live data to maintain response alignment with policy. Agent-MCP frameworks demonstrate how parallel execution with intelligent task management creates AI teams where backend agents handle APIs while frontend agents build UI components. This approach transforms development from single-agent bottlenecks into coordinated collaboration where agents follow specific patterns optimized for their roles.

Thanks to the growing number of developers contributing to MCP databases, APIs, and enterprise systems, organizations are able to obtain ready configurations and vetted integrations without the need for custom coding. The access to the tools is safe and transparent, and is scalable to the ecosystems. Security and oversight are required for ecosystems to scale, along with security models providing permission on a granular level, as well as on-overbound auditing. These are the principles on which MCP was designed. This makes MCP the first layer of safe, unsupervised, and enterprise-scale AI systems. 

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.

“MCP extends the value of the semantic layer from BI tools to any AI application or agent,” says Dave Mariani, Founder and CTO of AtScale. “We implemented the AtScale MCP Server as a lightweight, containerized service, which can be deployed with minimal friction. And because it’s open, it’s designed to interoperate with any chatbot or AI agent that speaks the protocol.” Together, MCP and semantic layers 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.