Unveiling the Model Context Protocol
The advent of Large Language Models (LLMs) has undeniably revolutionized the landscape of artificial intelligence. However, one persistent challenge remains: effectively integrating these models with proprietary data. While LLMs are proficient in processing vast amounts of information from the internet, their true potential lies in serving as natural language interfaces for our applications and data, allowing us to harness their predictive and generative capabilities within a controlled environment.
To ensure the reliability and relevance of LLM outputs, various strategies have emerged, each designed to bridge the gap between these models and specific datasets. These strategies range from creating custom embeddings and employing retrieval-augmented generation (RAG) techniques – leveraging graph databases to uncover intricate relationships within data – to utilizing LLMs to distill and present data obtained through OpenAPI calls triggered by user prompts. Furthermore, the plug-in model popularized by OpenAI’s ChatGPT offers another avenue for data integration. Among these diverse approaches, the Model Context Protocol (MCP) stands out as a promising solution for standardizing the connection between LLMs and external data sources.
The Model Context Protocol, unveiled by Anthropic in late 2024, represents a significant step towards streamlining the interaction between LLMs and user-specific data. This protocol underscores the critical role of context in AI applications, acknowledging that AI’s ability to deliver coherent and meaningful outputs depends on the availability of relevant contextual information. Relying solely on chat prompts to elicit sensible responses is, at best, an exercise in optimism and, at worst, a recipe for inaccurate or misleading results. To build robust, semi-autonomous agents capable of orchestrating workflows around our data, a dependable mechanism for delivering that data to these agents is essential.
As an open-source initiative, MCP provides SDK implementations for a wide array of programming languages, accompanied by comprehensive documentation within its GitHub repository. This documentation empowers developers to implement or utilize MCP servers effectively. The project’s description of MCP as a ‘USB-C port for AI applications’ aptly captures its essence, highlighting its ability to standardize connections to diverse data sources. Built upon a standardized foundation, MCP readily integrates with existing projects, seamlessly interfacing with various LLMs and inference providers.
MCP’s architecture follows a well-established client/server model, where a broker assumes the responsibility of translating MCP requests into local or remote requests. This design mirrors the functionality of interface definition languages like CORBA, transforming MCP into a versatile interoperability layer that facilitates seamless switching between information sources and LLM applications. Leveraging a JSON RPC connection, MCP enables fine-grained control at the individual user level through tools like Azure API Management.
MCP fosters the development of generic interfaces for AI-driven code, evidenced by its increasing adoption across Microsoft’s AI development platforms. From its integration within the Semantic Kernel model orchestration tool to its deployment as MCP servers compatible with Azure OpenAI and Azure AI Foundry, MCP is rapidly gaining traction. Microsoft is also enhancing Azure API Management with features to control data access based on user credentials, further solidifying MCP’s role in secure and governed AI deployments. The protocol’s emphasis on context is crucial for ensuring that AI models produce accurate and relevant outputs, minimizing the risk of generating misleading or nonsensical responses. This is particularly important in applications where the stakes are high, such as in healthcare, finance, and legal domains.
The adoption of MCP also promotes interoperability between different AI systems and platforms. By providing a standardized interface for data access, MCP allows developers to easily integrate different LLMs and data sources into their applications, fostering a more collaborative and innovative AI ecosystem. This interoperability is essential for driving the widespread adoption of AI and ensuring that its benefits are accessible to a broader range of users. Furthermore, the open-source nature of MCP encourages community involvement and collaboration, leading to continuous improvements and enhancements to the protocol. Developers from around the world can contribute to the project, sharing their expertise and helping to address emerging challenges in AI integration.
Azure MCP Server: An Early Implementation
The open-source Azure MCP Server, recently released in public preview, exemplifies an early implementation of MCP on Microsoft’s platform. This server serves as a common broker for AI access to key Azure services, adhering to the open-source principles embraced by many recent Azure projects. Its code is readily accessible on GitHub. The Azure MCP Server provides access to a substantial portion of the Azure platform, including databases, storage solutions, and services like the Azure CLI.
The inclusion of support for the Azure CLI (and the Developer CLI) is particularly noteworthy, as it empowers MCP-driven agents to interact with Azure directly, treating MCP calls as operators. This capability paves the way for building agents that offer a natural language self-service interface to Azure. For instance, an agent could take a description of an infrastructure and automatically generate the ARM templates required for its deployment. Extending this concept further, one could envision a multimodal agent that analyzes a whiteboard sketch, derives a description of the necessary resources, and then deploys the infrastructure, enabling immediate code development. Additional system administration services accessible through the Azure MCP Server encompass listing current resource groups and leveraging KQL to query Azure Monitoring logs.
The Azure MCP Server’s role as a central broker for AI access to Azure services significantly simplifies the process of integrating AI applications with the Azure ecosystem. Developers can leverage the server to seamlessly access and interact with a wide range of Azure resources, without having to worry about the complexities of managing individual service connections. This streamlined approach accelerates the development process and reduces the risk of errors. Moreover, the Azure MCP Server’s support for the Azure CLI provides a powerful tool for automating Azure management tasks. By enabling agents to interact with Azure directly through natural language commands, the server empowers users to manage their Azure infrastructure more efficiently and effectively.
The ability to generate ARM templates from natural language descriptions is a particularly compelling feature of the Azure MCP Server. This capability can significantly reduce the time and effort required to deploy complex Azure infrastructures, allowing developers to focus on building and deploying their applications. The vision of a multimodal agent that can analyze whiteboard sketches and automatically deploy the corresponding infrastructure represents a significant step towards democratizing access to Azure and empowering users to leverage the platform’s capabilities without requiring specialized expertise. Furthermore, the inclusion of support for KQL queries enables agents to proactively monitor Azure resources and identify potential issues, improving the overall reliability and performance of Azure deployments.
The Azure MCP Server also enhances the security and governance of AI deployments on Azure. By providing a centralized point of control for AI access to Azure resources, the server allows administrators to enforce security policies and monitor activity more effectively. This is particularly important in regulated industries where compliance with security standards is paramount. The integration of Azure API Management with MCP further strengthens security by enabling fine-grained control over data access based on user credentials.
Integrating Azure MCP Server with GitHub Copilot Chat
Because it adheres to the MCP standard, the Azure MCP Server seamlessly integrates with any AI tool that supports MCP, such as the GitHub Copilot Agent Mode. By simply adding the server to your tenant, you can begin posing questions through Copilot, either directly or via the Visual Studio Code integration. This latter option provides an effective means of learning how to leverage MCP and construct prompts for your own MCP-based AI applications.
Currently, Microsoft has not yet released a dedicated MCP tool for its programming languages, necessitating the use of official SDKs to develop custom code. With support for TypeScript, C#, and Python, developers have access to the necessary tools to create their own Azure MCP agents. Experimentation can be conducted within Visual Studio Code, utilizing existing Azure credentials.
The server operates on your development PC and requires Node.js. Installation is accomplished directly from the project’s GitHub repository into VS Code. Once installed, ensure that both the GitHub Copilot and GitHub Copilot Chat extensions are configured to use the experimental agent mode (accessible via the VS Code settings tool). Subsequently, open the GitHub Copilot chat pane and switch to agent mode. Verify that the Azure MCP Server is installed within the tools dropdown. You can now submit queries, such as ‘List my Azure subscriptions.’
The resulting tool proves invaluable for anyone working with Azure and extends beyond Copilot integration. The Azure MCP Server can be installed wherever Node.js is supported, enabling its integration into custom agents.
The seamless integration of the Azure MCP Server with GitHub Copilot Chat provides a convenient and intuitive way for developers to interact with Azure using natural language. By leveraging the Copilot Agent Mode, users can easily ask questions about their Azure resources and perform tasks without having to navigate the Azure portal or write complex scripts. This integration accelerates the development process and empowers developers to be more productive.
The availability of official SDKs for TypeScript, C#, and Python allows developers to create custom Azure MCP agents tailored to their specific needs. This flexibility enables developers to build sophisticated AI applications that can automate a wide range of Azure management tasks. The ability to experiment with these agents within Visual Studio Code, using existing Azure credentials, provides a convenient and safe environment for testing and refining AI-powered solutions. The fact that the server operates on a development PC and requires Node.js makes it easy to get started with Azure MCP development, without requiring complex infrastructure setup.
The Azure MCP Server’s integration with GitHub Copilot extends the capabilities of Copilot beyond code completion and suggestion. By enabling Copilot to interact with Azure resources, the Azure MCP Server transforms Copilot into a powerful AI assistant that can help developers manage their Azure infrastructure and accelerate their development workflows. This integration represents a significant step towards making AI an integral part of the software development lifecycle.
MCP’s Role in Azure AI Foundry
Microsoft is rapidly expanding its portfolio of MCP tools, exposing existing functionalities through MCP or enabling their consumption within agentic applications. This rapid rollout includes tools for Copilot Studio’s no-code agent development, announced during the writing of this article.
Azure AI Foundry, Microsoft’s primary development platform for large-scale AI application development, is also actively developing an MCP Server to complement Azure’s AI Agent Service. This integration aims to connect agents running within Azure AI Foundry with clients operating as part of other AI applications.
This service allows you to quickly repurpose existing AI code and services and link them to new applications. Services such as Fabric expose their agent features as AI Agent Service endpoints, enabling AI applications to seamlessly connect to core line-of-business data, providing essential grounding to mitigate the risks of hallucination and errors.
Upon installation, the server provides a set of MCP actions for connecting to agents and sending them queries. It can also list available agents or utilize a default agent for specific tasks. Support for conversation threads is included, providing agents with a basic semantic memory for contextual conversations. You will need the Azure AI Agent Service agent IDs to invoke them using MCP.
The server is implemented in Python and can be installed using the Azure CLI via pip. Alternatively, a TypeScript version is available for those who prefer it. Similar to the Azure MCP Server, this server operates outside of the AI Foundry environment, allowing it to be installed on a development PC or as part of a cloud-hosted application within its own container or VM, with support for Windows, macOS, and Linux.
The development of an MCP Server for Azure AI Foundry underscores Microsoft’s commitment to making MCP a central component of its AI strategy. By integrating MCP into its primary development platform for large-scale AI applications, Microsoft is providing developers with the tools and infrastructure they need to build and deploy sophisticated AI solutions that leverage the full power of the Azure ecosystem.
The ability to connect agents running within Azure AI Foundry with clients operating as part of other AI applications is a key enabler of AI interoperability. This capability allows developers to seamlessly integrate different AI systems and components into their applications, fostering a more collaborative and innovative AI ecosystem. The fact that services such as Fabric expose their agent features as AI Agent Service endpoints further enhances interoperability by providing a standardized interface for accessing and interacting with these services.
The inclusion of support for conversation threads in the Azure AI Foundry MCP Server provides agents with a basic semantic memory, enabling them to maintain context across multiple interactions. This capability is essential for building conversational AI applications that can understand and respond to user queries in a natural and intuitive way. The availability of both Python and TypeScript implementations of the server provides developers with flexibility in choosing the programming language that best suits their needs. The ability to install the server on a development PC or as part of a cloud-hosted application provides developers with a variety of deployment options, allowing them to choose the infrastructure that best meets their requirements.
The Azure AI Foundry MCP Server’s integration with the Azure AI Agent Service further strengthens the connection between AI applications and core line-of-business data. By providing essential grounding, this integration mitigates the risks of hallucination and errors, ensuring that AI models produce accurate and reliable outputs. This is particularly important in applications where the stakes are high, such as in healthcare, finance, and legal domains.
Leveraging MCP Servers from Semantic Kernel AI Applications
As an open standard, MCP enables server compatibility with any client. The GitHub repository provides instructions on how to establish a connection using Anthropic’s Claude Desktop, but the true value lies in building custom agent workflows within Semantic Kernel.
Microsoft provides sample code demonstrating how to integrate MCP support into a Semantic Kernel orchestration, treating it as a kernel plug-in that integrates with familiar function calls. These integrations can be wrapped as agents and accessed as needed. While MCP integration within Semantic Kernel is still under development, it seamlessly integrates with its existing feature set, requiring minimal additional code to expose MCP tools from a server to AI applications.
The open-source nature of MCP ensures that it is compatible with a wide range of AI clients and servers. The availability of instructions for connecting to Anthropic’s Claude Desktop demonstrates the interoperability of the protocol and its ability to connect different AI systems. However, the true power of MCP lies in its ability to enable the creation of custom agent workflows within Semantic Kernel.
By treating MCP as a kernel plug-in, developers can seamlessly integrate MCP tools into their Semantic Kernel orchestrations. This integration allows developers to build sophisticated AI applications that leverage the full power of both Semantic Kernel and MCP. The ability to wrap these integrations as agents and access them as needed provides a flexible and modular approach to AI development.
The fact that MCP integration within Semantic Kernel seamlessly integrates with its existing feature set, requiring minimal additional code, makes it easy for developers to get started with MCP. This integration allows developers to leverage their existing Semantic Kernel skills and knowledge to quickly build AI applications that leverage the power of MCP. The ability to expose MCP tools from a server to AI applications further enhances the flexibility and modularity of the Semantic Kernel platform.
Tools like MCP are critical components of a modern AI stack, providing a standardized approach to building discoverable interfaces for both local and remote applications. Once defined, MCP tools are easily invoked, with a server providing a list of available tools and MCP providing LLMs with a standardized means of calling those tools and utilizing their outputs. This approach significantly contributes to providing a universal grounding tool for AI applications, working with standard APIs, database queries, and AI agents alike. The standardized interfaces ensure that different AI components can communicate and interact effectively, regardless of their underlying implementation.