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Patrick Chan
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Posted on • Edited on • Originally published at Medium

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Does Model Context Protocol (MCP) Spell the Death of RAG?

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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Top comments (3)

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thinger_soft_f80ea0a2c7c3 profile image
Thinger Soft • Edited

I think this article is a little off target.
MCP defines an LLM-agnostic, composable and discoverable way of doing things, wich is great and opens up to tons of possibilities.
But MCP doesn't provide new tecniques to interact with LLMs.
You could do function based, active context retrieval before MCP and that approach has proven to be less effective than preemptive RAG with current models.
I don't see how MCP could change something in this regard.

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patrick_chan_0922a197d89d profile image
Patrick Chan

Agreed that MCP mainly provides standardization, but as you pointed out, it also unlocks many possibilities. One of those could be a tool that replaces RAG—not because it wasn’t possible before, but because MCP creates a market for such tools. If that happens, it would simplify things by removing the need for a dedicated preemptive workflow.

For example, imagine using an off-the-shelf Claude desktop to work with corporate documents just by adding an MCP server. There’s no way to insert your favorite RAG pipeline into these products, but with MCP, tools could provide context dynamically. A product that achieves RAG-like performance through standard MCP would be a welcome improvement. And given the incentives for tool-based approaches, I think there’s a real chance this could happen.

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thinger_soft_f80ea0a2c7c3 profile image
Thinger Soft • Edited

Hello Patrick,
what I was trying to point out is that it's not RAG vs MCP.
You can do many different things in many different ways.
You can take a monolithic RAG system and place an LLM-like API in front of it to enable integration with general-purpose clients.
A RAG pipeline can be designed to be extendable via MCP tools, allowing it to function as an MCP client.
If your point is that MCP makes basic RAG-like functionality more accessible to a broader audience, I agree.
However, at present, non-trivial RAG systems cannot be replaced by function calling alone, and MCP does not change that.

That being said, I believe MCP is a significant step forward, and I have been looking forward to its introduction.
As AI models continue to advance, we will delegate more and more to them, and MCP’s role will become increasingly important.
Eventually, RAG may no longer be necessary, but this will be a result of AI model evolution rather than MCP itself.

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