Retrieval-Augmented Generation (RAG) has been a cornerstone for enabling Large Language Models (LLMs) to access external knowledge. Its process is elegant and effective: a user query is transformed into an embedding and matched against a knowledge base filled with pre-chunked and embedded data. These chunks, carefully designed for semantic relevance and sized to fit within the LLM’s input constraints, are retrieved and appended to the input prompt. This augmentation allows the LLM to generate accurate, grounded responses, reducing hallucinations and improving reliability.
RAG operates in the pre-prompt phase. Developers have spent considerable effort refining embedding models, optimizing retrieval algorithms, and ensuring high-quality contextual augmentation. It’s a proven system, providing a structured pipeline to help LLMs handle complex or context-heavy queries.
Enter Model Context Protocol (MCP), a new standard that defines how LLMs interact with external systems. It establishes a uniform interface, enabling LLMs to call various tools during inference. These tools can handle a range of tasks, such as scheduling appointments, executing computations, or retrieving information. With MCP, developers don’t need to create custom solutions for every interaction; they can rely on a standardized framework to integrate tools into workflows.
One tool that could be provided via MCP is a context-retrieval tool — a mechanism for fetching relevant external information based on a query. This capability would allow an LLM to request specific knowledge during inference. If an LLM recognizes it lacks certain information to answer a query fully, it can use such a tool to fetch that context on demand. This begs the question: if LLMs were to handle context retrieval during inference, is RAG still needed?
This shift could fundamentally change how we think about context retrieval. MCP’s standardized interface would mean that developers working on a system with an MCP client no longer need to concern themselves with building or incorporating RAG for context retrieval. Instead, context fetching becomes just another tool the LLM can call on demand. While the ingestion process — structuring, embedding, or chunking data — remains vendor-specific, the retrieval process is abstracted into an MCP-compliant tool. This standardization simplifies development and shifts the focus from building retrieval systems to leveraging them as part of an LLM-driven, tool-enabled ecosystem.
Perhaps more importantly, if LLMs can use tools to query a knowledge base or search engine in real time, the decades of advancements in search technology might already be enough to solve much of the context retrieval problem. Consider how search engines like Google are evolving: their current model first runs a traditional search and then uses an LLM to summarize the results before presenting them to the user. This is conceptually similar to what an LLM might do when using an MCP-enabled retrieval tool — executing a search query, analyzing the returned results, and summarizing them. With this approach, the focus might shift away from pre-chunking and embedding data, relying instead on state-of-the-art search techniques, refined by the LLM’s ability to contextualize and summarize the information dynamically.
That said, pre-chunking and embedding research could still play a role — but perhaps not in the way we’ve traditionally thought. Instead of focusing solely on improving accuracy, the goal might shift toward cost optimization. If knowledge bases can be pre-summarized or structured in ways that reduce the volume of information returned by a search tool, the savings in processing time and token costs could be significant. This optimization could become critical in scenarios where large-scale knowledge retrieval is necessary but computational resources are limited.
Another advantage of MCP-enabled context retrieval is the potential for the LLM to iterate and tune its queries dynamically. Traditional RAG workflows typically retrieve static results based on a single embedding match, with no opportunity to refine or adjust. In contrast, an LLM driving the retrieval process could adapt its queries on the fly, ensuring it gets precisely the information it needs. This kind of iterative search, paired with the ability to interpret and summarize results, could result in more relevant and precise context than static pre-prompt workflows ever could.
If this approach proves viable, the RAG process might evolve into a backend component rather than a central workflow. Instead of pre-processing knowledge bases into embeddings and relying on static pipelines, developers might focus on MCP tools that allow LLMs to fetch and refine context dynamically. RAG’s expertise in retrieval logic doesn’t vanish but becomes part of the toolset available to the LLM, optimized for efficiency and cost savings rather than being a standalone process.
The shift to MCP-controlled context retrieval raises exciting possibilities. By leveraging decades of search advancements and allowing LLMs to dynamically manage their own context needs, we may not need to reinvent the wheel. Instead, we can enhance existing techniques while reducing the complexity and rigidity of traditional RAG workflows. It’s not the end of RAG — it’s a reimagining of how context retrieval is executed, with the LLM taking the lead and dynamic tools providing the support.
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