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API Protocol Strategy | geminiPromptEngineer

Architectural Overview

We are building a tool intended to feed context (code, documentation, zip archives) into Large Language Models like Gemini. To achieve a "Clean Room" architecture that is both scalable and future-proof for AI agents, we must select the right communication protocols. This report analyzes and recommends a hybrid approach utilizing MCP for agent interoperability, tRPC for robust backend communication, and SSE for streaming responses.

Strategic Recommendations

Explore the three core pillars of the proposed architecture.

Primary Strategy 🤖

Model Context Protocol (MCP)

The emerging standard for connecting AI models to external data.

Strategic Value High
App Layer ⚡

tRPC (TypeScript RPC)

End-to-end type safety for React Frontend <-> Backend communication.

Implementation Effort Low
Streaming 🌊

Server-Sent Events (SSE)

Native protocol for streaming token-by-token generation from Gemini.

Complexity Low

Select a Protocol

Click on one of the cards above to see the specific workflow, implementation details, and strategic justification for that protocol.

Implementation Concept

Select Protocol 
// Code preview will appear here based on selection.
// Select MCP, tRPC, or SSE above.

Protocol Comparison Matrix

Evaluating Impact vs. Effort to determine priority.

High
Strategic Value
Medium
Avg Implementation Effort

Implementation Roadmap

Recommended execution path for the "Clean Room" architecture.

1

Tactical Foundation: tRPC

Connect your React Frontend to the Backend. Share the `ExtractedFile` type definition to ensure type safety. Establish the base connection.

Immediate Priority
2

Strategic Wrapper: MCP

Wrap the backend logic in an MCP interface. This treats the tool as a "Resource" that AI agents can actively call independently.

Future Proofing
3

Optimization: SSE

Implement Server-Sent Events to handle token-by-token streaming from the Gemini API, ensuring a responsive user experience.