MiroThinker 1.5 adopts a "scientist" mode with Interactive Scaling, runs a hypothesis‑evidence loop, scores 56.1 on the BrowseComp benchmark—close to Gemini DeepSearch’s 59.2—while supporting up to 400 tool calls, 256K context, and delivers detailed research reports, all as an open‑source project on GitHub.
This article analyzes production‑grade issues of a multi‑agent React framework—such as long ToolCall latency, context bloat, missing intermediate states, loop control, and supervision gaps—and presents concrete XML‑based tool‑call prompts, context‑compression techniques, summary tools, and a plug‑and‑play MCP supervisor that together improve performance, reliability, and user‑facing output quality.
This article walks through how Java developers can connect to Python‑based AI services using the Model Context Protocol (MCP), compares STDIO and SSE transports, explains why Spring AI’s MCP support is limited, and shows a complete implementation with the raw MCP Java SDK and OpenAI client to invoke tools like Blender from Java code.
