Model Context Protocol (MCP) differs from REST, GraphQL, and gRPC in its focus on managing contextual interactions between clients and services, rather than just data transfer. REST relies on stateless, resource-oriented interactions using HTTP methods (GET, POST, etc.), treating each request as independent. GraphQL prioritizes flexible querying, letting clients request specific data structures. gRPC emphasizes high-performance RPC (remote procedure calls) with strict service contracts via Protocol Buffers. MCP, in contrast, centers on maintaining and evolving shared context—like session state or dynamic parameters—across interactions, making it suitable for scenarios where ongoing context (e.g., user behavior, model state) directly shapes responses.
For example, REST APIs often require clients to re-send context (e.g., authentication tokens, filters) with each request. In a shopping cart API, clients repeatedly send user IDs and item IDs. GraphQL reduces over-fetching but still treats queries as isolated operations. MCP could instead track the cart’s state server-side, updating it incrementally. Similarly, gRPC excels at low-latency communication (e.g., microservices exchanging stock trade data) but doesn’t inherently manage context beyond individual calls. MCP would formalize how services share and update context—like a fraud detection system propagating risk scores across transactions without repetitive client input.
MCP’s structure also differs technically. REST uses standard HTTP, GraphQL relies on a query language, and gRPC uses Protocol Buffers and HTTP/2. MCP might define its own protocols for context handshake, versioning, or synchronization. For instance, a chatbot using MCP could retain conversation history across API calls, whereas REST would require embedding the history in every request. This reduces payload size and server reprocessing. While gRPC supports streaming for real-time data, MCP could extend this to context-aware streams, like adjusting video quality based on network conditions tracked over time. These distinctions make MCP a niche tool for stateful, context-driven systems rather than general-purpose data exchange.