Model Context Protocol (MCP) is an open protocol designed to standardize how applications connect with language models (LMs). Think of MCP as being similar to a USB-C port, not for hardware, but for AI-driven systems. It provides a structured way for applications to interact efficiently with data sources, workflows, and tools. The three main features of MCP are resources, prompts, and tools. Resources consist of context and data that the user or model can utilize. Prompts are templated messages and workflows that guide interactions. Tools are functions that a language model can execute to complete specific tasks. This standardized approach makes MCP useful for integrating applications in a clear and repeatable way.

The concepts in MCP have noticeable similarities with kontekst.cloud, a platform that organizes systems around the central concept of “context.” Most features in MCP align directly to kontekst.cloud’s terms. Resources in MCP correspond to content in kontekst.cloud. Tools translate to actions, and prompts could align with agents or actions. However, prompts are tricky to define in kontekst.cloud since they are used differently. One suggestion is to treat them purely as templated messages and separate workflows as their own distinct concept. Unlike MCP, kontekst.cloud introduces threads that capture logs and process information, extending beyond the limited technical logging seen in MCP. This ability to store execution histories helps define workflows and track processes in greater detail.

Some challenges exist with terms like “resources” and “data,” as they are too broad and often end up encompassing everything. Kontekst.cloud has made efforts to be more precise by splitting features into content, process data, and actions. The platform uses an endpoint called /data to store all information related to features, but alternatively, /resources could be used. However, the generic nature of these terms still poses some risk of overlap between concepts. Despite this, the flexibility built into kontekst.cloud allows substantial customization, which makes implementing MCP on the platform relatively straightforward.

Kontekst.cloud’s design also enables support for alternative protocols like SOLID or other semantic web technologies. By adding a compatible layer, the platform can easily integrate standards like MCP while retaining the ability to work with other options. This adaptability positions kontekst.cloud as a versatile tool for building interoperable systems. Whether working with structured standards like MCP or experimenting with decentralized architectures supported by protocols like SOLID, kontekst.cloud provides the foundation for highly flexible implementations.

An important distinction between MCP and kontekst.cloud lies in the concept of context itself. In kontekst.cloud, context operates as the central organizing principle and can be seen as the “server” that ties together content, actions, workflows, and threads. MCP lacks this central concept and instead ties resources and tools to individual servers. To bridge this gap, kontekst.cloud could represent each context as its own independent server, assigning a root URL to each. This modular approach enhances scalability and allows workflows to be tied directly to user-specific or application-specific contexts, creating a more personalized experience.

Although MCP excels as a standardized integration protocol, kontekst.cloud takes these concepts further by emphasizing context as the foundation for organizing data and processes. This focus enables richer workflows and simplifies the design of reusable systems. With its ability to support MCP and other protocols, kontekst.cloud isn’t limited by any single system but instead embraces interoperability as a core strength. By combining the standardization provided by MCP with the context-driven modularity of kontekst.cloud, developers can build more scalable and flexible applications tailored to diverse needs.