Big jump
AI agents and agent workflows are the current buzzwords among developers and technical decision makers. While they certainly deserve community and ecosystem attention, there is less emphasis on one of the most important pieces of the agency puzzle: integrating legacy systems and line-of-business applications. I recently discussed how implementing AI agents compares to a complex enterprise application integration project.
Anthropic, a leading provider of AI models, has proposed a protocol and architecture for providing language models with the necessary context obtained from external systems. The Model Context Protocol, as its name implies, defines how to connect existing data sources such as file systems, relational databases, code repositories, and almost anything else to LLMs and agents.
Anthropic’s Model Context Protocol represents a significant step forward in AI integration, providing a universal standard that simplifies connections between AI systems and disparate data sources. This open source protocol addresses the challenges of accessing fragmented data, allowing for more efficient and AI-aware applications. By making it easier to interact with different data sources without any hassle, MCP improves the relevance and accuracy of AI-generated answers. This is a huge step forward in the development of AI capabilities.
MCP architecture
MCP operates through a sophisticated client-server architecture with three core components:
- MCP Servers: Act as a data gateway, exposing resources, tools and requirements to AI applications
- MCP customers: AI-powered tools that interact with these servers
- Communication layer: It provides secure and two-way data exchange between local and remote resources.
MCP architecture
MCP allows data to flow in both directions between AI models and external data sources, which makes AI applications more interactive and aware of their surroundings. The protocol serves as a base layer for integrating AI models with various tools, essential for developing more nuanced and functional AI applications.
By offering an open source framework, MCP simplifies tool integration for developers, reducing the need for custom implementations for each new data source. Designed to work in diverse environments, including low-code platforms and cloud services, MCP offers flexibility and adaptability.
Anthropic has already developed SDKs for Python and TypeScript, along with pre-built servers for enterprise systems like Google Drive, Slack, GitHub and Postgres.
The role of MCP in the development of AI agents
While there are many frameworks for building AI agents, almost all of them rely on tools that leverage the function calling capabilities of LLM. Although it is the right approach, this mechanism limits the number of tools and functions that LLM can map to a request or query.
Anthropic’s MCP significantly enhances the capabilities of AI agents by enabling direct, two-way communication with external systems. This advancement allows AI agents to access real-time information from external databases, manage file systems, and seamlessly interact with platforms like GitHub. Consequently, AI agents can autonomously perform complex tasks, such as streamlining software development workflows, thereby increasing efficiency and reducing the need for manual intervention.
By facilitating these interactions, MCP empowers AI agents to execute sophisticated operations in various domains, marking a fundamental advance in the evolution of AI agent workflows.
Learning from the Past: The Evolution of SOA
While MCP focuses on AI integration, it’s worth comparing it to SOA protocols, which have helped shape enterprise architecture. SOA protocols, such as SOAP, WSDL, and WS-*, were designed for communicating Web services using XML-based messages. They provide robust security and reliable error handling, suitable for enterprise-level applications, and provide comprehensive standards for service descriptions and additional features such as security and transactions.
In contrast, MCP is specifically tailored for AI model integration with external data sources, supporting two-way communication and enabling more dynamic AI interactions. It focuses on simplifying the integration of AI across different tools and platforms.
While SOA protocols have been essential to enable interoperability between disparate systems, MCP addresses the unique challenges of integrating AI models with disparate data sources and tools. MCP’s focus on two-way communication and specific AI integration makes it particularly well-suited to the evolving landscape of AI-driven applications.
Challenges and limitations of MCP
Undoubtedly, MCP has the potential to become a key part of making AI agent workflows possible and faster. These are workflows that use autonomous, goal-driven AI systems. It empowers AI agents to make informed decisions based on real-time data from various sources. The protocol’s ability to maintain context across different tools and datasets supports the development of more independent AI agents. By integrating multiple tools and data sources, MCP enables the coordination of complex AI-driven workflows.
The success and widespread adoption of protocols such as MCP is highly dependent on industry participation and standardization efforts. Standardization ensures that AI systems can work across different platforms and jurisdictions, essential for global companies. It helps build trust by ensuring that AI systems are transparent, reliable and secure. Clear guidelines reduce compliance complexity, lowering barriers to innovation and enabling faster development of AI products. Standards can include the principles of fairness, accountability and non-discrimination in AI systems. Adherence to international standards facilitates market access, especially for smaller enterprises. The development of AI standards fosters collaboration among stakeholders, ensuring inclusive and reflective best practices.
The participation of leading AI entities—such as OpenAI, Google, Microsoft, Meta, and Mistral—is essential in driving the standardization and adoption of protocols such as MCP. Their involvement can lead to the development of interoperable systems that adhere to common standards, reducing fragmentation and fostering innovation.
Drawing parallels with the evolution of protocols such as SOAP, WSDL, and the WS-* suite in the context of SOA, industry-wide acceptance was instrumental in establishing these as foundational technologies for Web services. Similarly, the collective adoption of MCP by key industry players can accelerate its integration into AI systems, promoting a cohesive and efficient AI ecosystem.
Anthropic’s Model Context Protocol represents a significant advance in AI integration, providing a standardized approach for connecting AI models to external data sources.
The success of MCP depends on broad industry adoption and the collaborative efforts of leading AI organizations to establish it as a standard protocol, thereby fostering a more integrated and efficient AI landscape.