In a Model Context Protocol (MCP)-based system, context flows through a structured sequence of processing steps, where each component accesses and modifies a shared context object. The protocol defines how this context is initialized, propagated, and synchronized across modules. At its core, MCP ensures that every part of the system—whether a service, function, or data processor—receives the necessary contextual information to perform its task. For example, in a multi-step workflow, the output of one module becomes part of the context for the next, allowing subsequent steps to build on prior results. This flow is managed through explicit rules for context updates, ensuring consistency and reducing dependencies between components.
The technical implementation typically involves a context container—a data structure that holds state, metadata, and intermediate results. When a component processes a request, it reads the current context, applies its logic, and writes updates back to the container. MCP enforces serialization and versioning to handle concurrent modifications, often using mechanisms like locks or optimistic concurrency control. For instance, in a distributed system, a user authentication service might add a “user_id” and “permissions” field to the context, which downstream services like billing or data access then use to enforce policies. The protocol also defines error-handling rules, such as rolling back context changes if a step fails, ensuring the system remains in a valid state.
A practical example is an e-commerce order processing system. When a user initiates a purchase, the context starts with their cart contents and shipping details. The inventory service first checks product availability, updating the context with stock status. The payment gateway then uses the context to process payment, appending transaction IDs or error codes. Finally, the shipping service generates a tracking number based on the finalized context. MCP ensures each service operates on the latest version of the context, avoids redundant data passes, and provides audit trails. This approach simplifies debugging, as developers can trace the entire workflow’s state evolution through the context log.