How Async Pipelines and MCP Can Boost RAG System Performance

Retrieval-Augmented Generation (RAG) systems typically run search processes sequentially, but async pipelines allow vector search, text search, and semantic caching to run in parallel, cutting retrieval latency significantly. For example, tasks that would take 17 seconds sequentially can complete in as little as 10 seconds using parallel threads. On the tool-calling side, the Model Context Protocol (MCP) provides a standardized interface that lets developers access shared functionality — such as weather data or RAG retrieval — without writing custom code each time. MCP exposes predefined functions and their descriptions, allowing the LLM to decide which function to invoke based on the query. Together, async pipelines and MCP aim to make RAG systems faster, more scalable, and accessible to a broader set of developers and applications.