How RAG and MCP Combine for Smarter Agentic Development

Developers are increasingly blending foundational model capabilities with real-time data access to boost relevance, accuracy, and operational utility. At the heart of this shift are two transformative technologies: Retrieval-Augmented Generation (RAG) and the newly released Model Context Protocol (MCP). RAG equips large language models (LLMs) to retrieve pertinent context before generating responses. MCP, on the other hand, offers a standardized protocol for exposing diverse data sources to those models. Together, they form a robust foundation for AI systems that bridge static knowledge and real-time information demands.

What is Retrieval-Augmented Generation (RAG)?

RAG was the first method to inject external data into LLM reasoning and gained popularity for this purpose. It fundamentally improves LLMs by allowing them to pull in external context during inference. Instead of relying solely on their pre-trained knowledge, RAG introduces a retrieval layer that supplies relevant documents or data to ground responses. This makes it ideal for working with unstructured content like articles, documents, and knowledge bases.

Historical context

Introduced in 2020 by Facebook AI Research (FAIR), RAG addressed a key limitation in static LLMs. In their seminal paper, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," FAIR demonstrated that combining dense retrieval with generative models like BART significantly improved accuracy for open-domain question answering. Since then, RAG has evolved with:

• Support for hybrid retrieval (dense + keyword)
• Integration with scalable vector databases
• Inclusion in frameworks like LangChain and LlamaIndex
• Adoption by major providers such as OpenAI, Anthropic, and AWS Bedrock

Key advantages:

• Reduced hallucinations: Grounds answers in verifiable data
• Real-time knowledge access: Pulls in current or proprietary knowledge
• Improved domain adaptability: Tailors outputs to specific industries or use cases

RAG works by retrieving data external to the model and then uses a generator to incorporate that into the LLMs response.

• Retriever: Locates relevant documents, typically using vector search
• Generator: Produces grounded responses using retrieved context

Performance depends heavily on the retrieval infrastructure—and is limited to technologies that support semantic similarity searches on large datasets, like vector databases, knowledge graphs, traditional databases with vector extensions, or full-text search engines. RAG’s primary role has been to inject meaningful bulk context into LLM inputs, dramatically improving their informativeness and accuracy.

"RAG is particularly useful in scenarios where the LLM's training data might be outdated or insufficient." — AWS, What is RAG?

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) provides a standardized and modular framework for facilitating how LLMs access and utilize context. It streamlines how models can connect to external data sources—particularly structured ones like APIs and databases. MCP can also support unstructured sources via its "Resources" model, as outlined at modelcontextprotocol.io/docs/concepts/resources.

Historical context

MCP was released in November 2024 to meet growing demand for secure, consistent, and structured data access within the LLM ecosystem. Designed for extensibility and control, MCP enables AI systems to retrieve and act on external data with precision and predictability.

Key milestones include:

• MCP specification released: Defined servers, clients, and schema-aware context management
• Emerging adoption: Beginning with OpenAI in March 2025, key organizations are adopting the MCP standard, including organizations like Bock, Replit, Sourcegraph, Google, and Microsoft

While RAG focuses on providing great context to prompts from mostly static (or slowly evolving) datasets, often in the form of unstructured data, MCP excels at providing a mechanism for LLMs to both explore additional dynamic information and take actions that affect the connected systems. It’s not just about surfacing fixed information—it’s about introducing live information and enabling LLMs to do something with that information.

The protocol covers:

• Standard interface for exposing model-ready context
• Standard client-server architecture
• Supports both structured (SQL, APIs) and unstructured (via Resources) data
• Built-in orchestration for context-aware actions

MCP standardizes the connective technology between external systems and LLMs. Responses to data queries are translated into structured context objects that the model can consume. Actions are performed through well-defined tools that allow for client or agentic interaction with systems. All the while, MCP servers are responsible for integrating with enterprise sources, applying data transformation rules, and managing access controls.

Integrating RAG and MCP: Why and when to use both

Together, RAG and MCP offer a powerful way to fuse unstructured and structured data into a seamless AI architecture. RAG provides flexible, semantic access to freeform text, while MCP enables governed, context-aware interaction with structured systems.

Complementary roles:

Technical integration:

1. Data ingestion: MCP connects to systems like CRMs and ERPs
2. Data transformation: MCP formats data for LLMs and optionally embeds it
3. Embedding & indexing: Embeddings are stored in a vector database
4. Retrieval & generation: RAG retrieves contextual snippets; LLM generates grounded responses

Even when MCP is used with Resources for unstructured data, RAG often plays a role—especially for generative enhancement. MCP can act as a preprocessing or augmentation layer, enabling structured context delivery without needing a separate embedding pipeline.

Real-world applications

Here’s how RAG and MCP can be used together to support three example applications:

Customer support agents

• RAG pulls in documentation and forum posts
• MCP connects to CRM for case history and user context

Sales intelligence agents

• RAG retrieves marketing assets and pricing sheets
• MCP integrates structured deal status and account metadata

Risk advisory agents

• RAG brings in policy documents and legal archives
• MCP accesses structured compliance records and transactional logs

Conclusion

The RAG + MCP combination provides two tools to inject external data into AI development. RAG provides LLMs with flexible access to high-quality external content for improved generation, while MCP supplies structured, orchestrated context for action-driven applications. Together, they form a comprehensive, scalable strategy for building intelligent systems that access, understand, and act within enterprise systems.