What is the Model Context Protocol (MCP)?

Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Think of the Model Context Protocol as "API for AI integrations". It provides a standardized interface that allows AI applications to communicate effectively with various external systems.

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The Model Context Protocol addresses a critical pain point in AI development: the fragmented landscape of AI tool integrations. Before MCP, developers had to create custom integrations for each data source or tool their AI applications needed to access. The Model Context Protocol changes this by providing a universal standard that works across different AI models and platforms.

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Key Features of Model Context Protocol

The Model Context Protocol offers several essential features that make it invaluable for AI developers:

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Universal Standardization

‍The Model Context Protocol provides a consistent interface across different AI applications and external services. This standardization eliminates the need for custom integration code for each new data source or tool.

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Security Design

‍The protocol incorporates measures that support safe data access and controlled tool execution, helping protect AI applications as well as the external systems they connect with.

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Bidirectional Communication

‍Unlike traditional API approaches, the Model Context Protocol enables two-way communication between AI applications and external resources, allowing for more sophisticated interactions and dynamic data exchange.

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Model Agnostic Architecture

‍The Model Context Protocol works with various AI models and platforms, making it a truly universal solution for AI integration challenges.

How Model Context Protocol Works

Understanding the Model Context Protocol architecture is crucial for effective implementation. The protocol operates on a client-server model with three core components:

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MCP Clients

MCP clients are AI applications that consume external resources and tools through the Model Context Protocol. These clients can be chatbots, IDE assistants, custom AI agents, or any application that needs to access external data or functionality. Popular MCP clients include Claude Desktop, various IDEs with AI assistance like Cursor, and custom-built AI applications.

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MCP Servers

MCP servers expose data sources, tools, and resources to MCP clients through the Model Context Protocol interface. These servers enable Large Language Models (LLMs) to securely access tools and data sources. An MCP server can provide access to databases, APIs, file systems, or any other external resource that an AI application might need.

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Model Context Protocol Communication

The communication between MCP clients and servers follows the Model Context Protocol specification, ensuring consistent behavior across different implementations. This communication includes:

• Resource Discovery: MCP clients can discover available resources from MCP servers dynamically, allowing for flexible and adaptable AI applications.
• Tool Invocation: The Model Context Protocol enables AI applications to invoke tools and functions provided by MCP servers, extending the capabilities of AI models beyond their training data.
• Data Access: Through the Model Context Protocol, AI applications can access real-time data from various sources, keeping their responses current and accurate.

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Model Context Protocol Architecture and Components

The Model Context Protocol architecture is designed for flexibility and scalability. Understanding its components helps developers create more effective integrations:

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Core MCP Primitives

The Model Context Protocol defines several core primitives that form the foundation of all interactions:

1. Prompts: Reusable templates that MCP servers can expose to clients, enabling consistent and structured interactions across different AI applications.
2. Resources: Within the Model Context Protocol, resources are defined as read-only, addressable content entities that a server makes available to its clients. These resources give MCP clients a standardized way to retrieve structured and contextual data, which can then be passed on to AI models for reasoning. Typical examples include system logs, configuration data, or external documents that provide context for intelligent processing.
3. Tools: Executable functions that MCP servers provide to clients, allowing AI applications to perform actions in external systems.

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Transport Layer in Model Context Protocol

The transport layer in the Model Context Protocol is designed with flexibility in mind, ensuring that it can support a wide variety of deployment scenarios. By offering multiple transport mechanisms, the protocol adapts easily to both small scale applications and enterprise-level systems.

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One of the key options is local transport, which is used when MCP servers and clients run on the same machine. This setup provides extremely fast and secure communication because it eliminates the overhead of network transfers. Local transport makes the Model Context Protocol especially efficient for on-device processing or environments where performance and security are critical.

For more complex infrastructures, the Model Context Protocol also supports network transport. This option enables communication with remote MCP servers, making it possible to build distributed architectures and deploy solutions in the cloud. Network transport extends the reach of the protocol beyond a single machine, which is essential for organizations looking to scale AI applications across multiple systems.

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Another important capability of the transport layer is streaming support. With streaming, the Model Context Protocol allows real time data transmission between servers and clients. This feature improves responsiveness, reduces latency, and enhances the overall performance of AI powered systems. Whether applied in conversational AI, live analytics, or automation workflows, streaming support makes MCP highly effective for time sensitive tasks.

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Together, these transport mechanisms (local, network, and streaming) make the Model Context Protocol adaptable, secure, and high-performing across a broad range of environments.

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Dawiso's MCP Integration: data sources for AI

Dawiso integrates seamlessly with the Model Context Protocol to create a powerful foundation for AI adoption. Acting as a true catalog of catalogs, Dawiso consolidates metadata, glossaries, and business documents within a single platform. By connecting all of these components through the Model Context Protocol, Dawiso prepares high-quality, trusted content that is ready to be consumed by AI agents. This ensures that organizations not only centralize their knowledge but also make it immediately usable for intelligent applications.

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How Dawiso Leverages Model Context Protocol

Unlike traditional solutions that often demand complex technical implementation, Dawiso provides MCP integration as a ready-to-use service. This approach eliminates lengthy setup processes and allows organizations to quickly transform their data ecosystem into AI-ready resources. Through this integration, Dawiso bridges the gap between governance requirements and advanced AI capabilities.

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Directory of Data Resources: Through its Model Context Protocol integration, Dawiso provides a unified directory of organizational data sources. Dawiso makes it easier for AI models to locate and reference where different resources are stored, ensuring that all knowledge assets are discoverable from a single catalog.

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Trusted Content Delivery: Through the Model Context Protocol, Dawiso ensures AI agents receive governed, compliant, and verified information.

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Seamless AI Integration: Your AI tools can communicate directly with Dawiso's data catalog through standardized MCP interfaces, eliminating the need for custom integrations.

Dawiso's MCP Implementation Focus

Dawiso specializes in providing MCP resources, exposing your data catalogs, documentation, glossaries, and governance policies as content that AI agents can reference and understand.  

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Through Dawiso's MCP resource integration, AI agents can:

• Access complete data lineage documentation
• Understand data dictionary definitions and metadata standards
• Have context for information usage and compliance  

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This resource-focused approach ensures AI agents have rich contextual knowledge about your data landscape while maintaining strict governance and security boundaries.

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Model Context Protocol Best Practices

When implementing AI assistants with access to your data management resources through the Model Context Protocol, following established best practices ensures maximum value and maintains organizational standards:

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Organizing Data Resources for AI Access

Structure your data catalog content to maximize AI assistant effectiveness:

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Consistent Documentation Standards: Maintain standardized formats for documentation to help AI assistants provide consistent and accurate information across different queries.

Hierarchical Information Architecture: Organize your data catalogs, glossaries, and documentation in logical hierarchies that AI agents can navigate efficiently when seeking specific information.

Regular Content Updates: Keep your metadata current and comprehensive, as AI assistants will reference the most recent version available through the Model Context Protocol.

Clear Metadata Definitions: Provide detailed, unambiguous definitions for all business terms and data elements to ensure AI assistants communicate accurately with users.

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Build Trust and Control

Ensure your MCP-enabled AI assistants support rather than compromise your governance objectives:

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Access Control Alignment: Coordinate with your MCP provider to make sure AI assistants access resources in line with your organization’s existing data policies and user permissions.

Audit Trail Maintenance: Maintain comprehensive logs of how AI assistants access and use your governance resources to support compliance reporting and risk management.

Content Sensitivity Management: Classify and appropriately restrict access to sensitive information, ensuring AI assistants only reference materials appropriate for each user's role and clearance level.

Consistency with Existing Policies: Verify that AI assistant responses align with established data governance policies and don't inadvertently contradict existing organizational standards.

Maximizing AI Assistant Value

Optimize how your team works with MCP-enabled AI assistants:

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User Training and Guidelines: Provide clear guidance to employees on how to effectively interact with AI assistants that have access to your governance resources, including what types of questions yield the best results.

Feedback and Improvement Cycles: Establish processes for collecting user feedback about AI assistant responses and use this information to improve your documentation and resource organization.

Integration with Workflows: Design AI assistant interactions to complement existing workflows rather than replace critical human oversight and decision-making processes.

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Conclusion

The Model Context Protocol represents a significant advancement in AI integration technology. By functioning as a universal translator, MCP enables seamless dialogue between AI systems and external data sources, tools, and services. As the protocol continues to mature and gain industry support, it will undoubtedly become an essential tool for developers building sophisticated AI applications.

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The Model Context Protocol's open standard approach, combined with its robust architecture and growing ecosystem, positions it as the foundation for the next generation of AI-powered applications. Whether you're building enterprise solutions, development tools, or consumer applications, the Model Context Protocol provides the standardized integration layer that can accelerate your development process and improve your application's capabilities.

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By adopting the Model Context Protocol now, developers can future-proof their AI applications and take advantage of the growing ecosystem of tools, resources, and integrations that the protocol enables. The future of AI development is connected, and the Model Context Protocol is the bridge that makes those connections possible.

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Dawiso strengthens the Model Context Protocol by acting as a metadata context layer for AI. With Dawiso, AI agents can interpret internal definitions, locate specific data sources, and operate within the established structures of your organization. This ensures that responses are not only relevant but also aligned with business standards, allowing AI to integrate seamlessly into existing processes and deliver real value across the enterprise.

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