MCP Foundry: A Practical Guide to Model Context Protocol Based on Google Gemini

MCP Foundry is a TypeScript practical solution based on the Model Context Protocol (MCP) and Google Gemini capabilities. It aims to address the context interaction challenges of large language models (LLMs), providing a complete toolchain from protocol implementation to Agentic AI workflow construction. It helps developers flexibly combine context management, tool calling, and model inference components to efficiently build complex Agentic AI applications.

With the improvement of LLM capabilities, external context interaction has become a key challenge. The MCP protocol defines a standardized mechanism to enable efficient interaction between models, tools, and context data. Its core value lies in decoupling the three major components (context management, tool calling, model inference), improving system maintainability, and laying the foundation for Agentic AI applications.

Developed in TypeScript, the code is organized in Monorepo form (sub-packages such as core protocol, server SDK, client SDK, toolset, sample applications, etc.). Turborepo is used as the build tool to orchestrate tasks, and pnpm workspace is used to achieve efficient dependency management and build caching.

Layered design: The transport layer handles JSON-RPC communication, the protocol layer parses MCP message formats, and the business layer implements tool logic. It provides a declarative tool registration API (defining input schema, output type, and execution function enables automatic handling of validation and response). It supports streaming responses (pushing intermediate results of long tasks via SSE).

Multiple integration modes: Direct connection mode (WebSocket/HTTP) is suitable for simple scripts, and MCP Hub service discovery mode is suitable for complex applications. Deep integration with Gemini: Inject tool descriptions into the model context, allowing Gemini to automatically determine whether to call external tools, enabling intelligent Agentic interaction.

Tool calling process: Model decides to call → Client verifies request legality → Routes to Server for execution → Returns results to inject into model context. It automatically handles retries, timeouts, and error recovery. Built-in call chain tracing (unique trace ID) facilitates debugging and performance optimization.

Provides preset workflow modes (sequential execution, parallel branching, conditional judgment, loop iteration). Supports combined construction of complex processes (such as multi-step interaction of code review agents). Introduces a memory mechanism to persist intermediate states, supporting long-term tasks and cross-session context retention.

MCP Foundry covers all aspects of Agentic AI development (Server/Client development, tool calling, workflow orchestration), providing a complete TypeScript solution for the MCP protocol. As the MCP ecosystem matures, such infrastructure will play a more important role in AI application development.