Client Streaming In MCP The-Force: Implementation Plan

In the realm of modern AI applications, user experience is paramount. One crucial aspect of this is the perceived latency when interacting with AI models. Currently, MCP The-Force buffers complete AI model responses before presenting them to clients. To address this, we propose implementing full client streaming support, allowing clients to see responses in real-time, word by word. This document outlines a strategic plan for this implementation, focusing on leveraging existing infrastructure and minimizing disruption.

Overview

Client streaming is a game-changer for applications relying on AI models. Imagine waiting for an entire paragraph to load before you can read the first word – that's the current experience with MCP The-Force. Now, envision words appearing on the screen as the model generates them, creating a much more engaging and responsive feel. This is the power of client streaming.

By implementing streaming support, we can significantly reduce the perceived latency for long responses, making interactions feel more natural and immediate. This is especially crucial for applications where real-time feedback is essential, such as conversational AI or live content generation.

The plan detailed here focuses on leveraging the existing FastMCP infrastructure, minimizing the need for extensive code changes and ensuring backward compatibility. This approach allows for a phased rollout, starting with internal streaming and gradually expanding to full client streaming capabilities.

The key to this implementation lies in the discovery that FastMCP 2.3+ already possesses built-in streaming support. This means we can tap into existing functionalities, rather than building from the ground up, significantly reducing the time and effort required for implementation.

Key Discovery: FastMCP Already Supports Streaming

This is huge, guys! FastMCP 2.3+ has built-in streaming support. We can leverage this existing functionality to make our lives easier. Here’s the breakdown:

• Tools can be annotated with @mcp.tool(annotations={"streamingHint": True})
• The context object provides ctx.stream_text(chunk) for sending incremental updates
• Both stdio and HTTP transports handle streaming properly
• Claude Code already renders streamed chunks

This discovery is a major win for our implementation plan. It means we don't need to reinvent the wheel; instead, we can focus on integrating our models with the existing streaming infrastructure. The @mcp.tool annotation allows us to easily flag tools as streaming-capable, while the ctx.stream_text(chunk) function provides a simple and efficient way to send data chunks to the client.

The fact that both stdio and HTTP transports already support streaming ensures that we can seamlessly integrate this functionality across different communication channels. Moreover, the fact that Claude Code is already rendering streamed chunks demonstrates the feasibility and effectiveness of this approach.

By leveraging these existing capabilities, we can significantly accelerate the implementation process and deliver the benefits of client streaming to our users sooner.

Current Streaming Support Status

Let's take a look at where our models stand right now in terms of streaming capability:

• ✅ Already streaming internally: o3, o4-mini, gpt-4.1 (OpenAI models)
• 🚫 Intentionally non-streaming: o3-pro (background-only due to long processing times)
• ⚠️ Could stream but don't: Gemini 2.5 Pro/Flash, Grok 3 Beta/4

It's fantastic that o3, o4-mini, and gpt-4.1 are already streaming internally. This provides a solid foundation for expanding streaming support across the platform. The o3-pro model, intentionally non-streaming due to long processing times, highlights the need for flexibility in our approach. Not all models are created equal, and some may be better suited for non-streaming execution.

The