ggRMCP – gRPC to MCP Gateway

Overview of ggRMCP

ggRMCP is a high‑performance Go gateway that bridges the gap between traditional gRPC services and the Model Context Protocol (MCP). By leveraging gRPC reflection or FileDescriptorSet files, it automatically discovers all services and methods exposed by a running gRPC server. Each discovered method is then translated into an MCP‑compatible tool, complete with JSON schema definitions derived from the original protobuf messages. This enables AI assistants—such as Claude—to invoke your gRPC endpoints directly, without any manual adapter code or service rewrites.

The value of ggRMCP lies in its zero‑code integration model. Developers can deploy it as a sidecar container alongside any existing gRPC service, regardless of the implementation language (Java, Python, C++, Go, etc.). The gateway handles all protocol translations in real time: JSON requests from the AI client are converted to protobuf, routed through HTTP/2 to the gRPC server, and the protobuf responses are serialized back to JSON for MCP consumption. Header forwarding, error mapping, and session management are all handled automatically, ensuring that the AI experience remains seamless and secure.

Key capabilities include:

• Dynamic tool generation from live service discovery, allowing new gRPC methods to appear in MCP tooling without redeploying the AI application.
• Schema validation using protobuf definitions, guaranteeing that requests and responses conform to expected types before they reach the backend.
• Stateful session handling, which is essential for complex conversational flows that need to maintain context across multiple gRPC calls.
• Rich documentation extraction from FileDescriptorSet comments, giving AI models access to method descriptions and parameter details for better prompt generation.

Typical use cases span a wide range of AI‑enabled workflows. For example, a financial analytics platform can expose its risk‑assessment gRPC service via ggRMCP, letting an AI assistant query real‑time market data and return actionable insights. In a manufacturing setting, sensor‑data gRPC endpoints can be surfaced to an AI dashboard that predicts maintenance needs. Any scenario where real‑time, typed data exchange is required between an AI model and existing microservices can benefit from ggRMCP.

Because it operates as a language‑agnostic sidecar, ggRMCP offers a unique advantage: zero disruption to legacy services. Teams can adopt AI capabilities incrementally, starting with a small set of critical gRPC methods exposed as MCP tools, and scale the gateway to cover additional services over time. This incremental approach reduces risk while accelerating AI integration across distributed systems.