#RAG and MCP: How Model Context Protocol Changes the Retrieval Landscape

📅 05.01.26 ⏱️ Read time: 7 min

Model Context Protocol (MCP) is a new open standard for connecting AI models to external data sources, tools, and services. It's quickly becoming part of every AI practitioner's vocabulary — and it's changing the conversation about RAG.

MCP doesn't replace RAG. But it changes the architecture of how AI systems access data, and understanding the relationship between RAG and MCP is essential for building AI systems in 2025.

Model Context Protocol (MCP) is an open standard, introduced by Anthropic in late 2024, that defines a standardized way for AI models to connect to external data sources and tools.

Before MCP, every AI application that needed to connect a language model to external data built its own integration: custom tool definitions, custom API wrappers, custom data connectors. The integrations were not interoperable — a tool built for one AI framework didn't work with another.

MCP defines a universal protocol: a standard interface that any AI model (or AI agent host) can use to communicate with any data source or tool that implements the protocol. A source that exposes an MCP server can be accessed by any AI system that supports MCP, without custom integration code.

Think of MCP as USB for AI data access: a standard connector that works everywhere, instead of a different cable for every device.

MCP defines two sides:

MCP Server: exposes resources (data sources) and tools (callable actions) through the standardized protocol. A server might expose:

• Documents from a knowledge base (readable resources)
• Database query capabilities (tools the model can call)
• File system access (readable resources)
• API endpoints (tools)

MCP Client: an AI model host (Claude Desktop, an agent framework, a custom application) that connects to MCP servers and provides the AI model with access to what they expose.

The flow:

1. The AI model is configured with one or more MCP server connections
2. The model is told what resources and tools are available via those servers
3. When the model needs external data or needs to take an action, it requests it through the MCP protocol
4. The MCP server returns the data or executes the action

MCP supports three primitives:

• Resources: data that the model can read (documents, database records, file contents)
• Tools: actions the model can execute (queries, API calls, computations)
• Prompts: reusable prompt templates that can be served to the model

RAG and MCP solve related but distinct problems.

The core difference: RAG is an architecture built around pre-indexed vector search. MCP is a protocol for live data access. RAG answers "what's most semantically similar to this query?" MCP answers "what does the model need right now, and how does it get it?"

The most powerful AI systems use both:

MCP as the transport layer for RAG. A RAG pipeline can be exposed as an MCP server. The server implements a search tool: the model sends a query, the server performs vector retrieval and returns the relevant chunks. From the model's perspective, it's making an MCP tool call. Underneath, it's RAG.

This is a significant architectural benefit: the RAG implementation becomes interoperable. The same RAG server can be accessed by any MCP-compatible AI host — Claude Desktop, a custom agent, a third-party application — without rebuilding the integration.

MCP for live data; RAG for document knowledge. In a complex AI system:

• RAG (accessed via MCP) handles document-based knowledge retrieval: internal policies, product documentation, support content
• Direct MCP tool calls handle live, structured data: database queries, real-time API calls, current account state

The model decides dynamically what to retrieve from where — document knowledge via RAG, live data via direct tool calls — coordinated through the same MCP protocol.

Custom models via MCP. A trained ML model (churn prediction, classification, anomaly detection) deployed as a REST API can be wrapped in an MCP server and exposed as a tool. The AI model can then call the custom model within a conversation — bridging language model capabilities with custom prediction capabilities.

Use RAG when:

• You have a large document corpus that needs semantic search
• Retrieval should happen automatically on every query
• You need to ground answers in specific source documents with attribution
• Your knowledge base is mostly unstructured text

Use MCP when:

• You need to connect an AI model to structured data sources (databases, APIs)
• You want interoperability across multiple AI hosts and frameworks
• The model should decide when to retrieve, rather than retrieval being automatic
• You're building agentic systems that need to take actions, not just retrieve information

Use both when:

• You need document retrieval AND live structured data access
• You want the RAG pipeline to be interoperable with multiple AI systems
• You're building a complex AI assistant that reasons about both documents and real-time data

MCP is significant not because it replaces existing patterns but because it standardizes them. As the ecosystem matures:

• More data sources and tools will expose MCP servers (databases, SaaS tools, internal systems)
• AI hosts will increasingly expect MCP as the integration standard
• RAG pipelines will be wrapped in MCP servers as a deployment pattern
• Custom models will be exposed as MCP tools for consumption by AI agents

The team building AI systems today should understand MCP not as a replacement for what they're already doing, but as the emerging standard that will make their AI components more composable and interoperable.

Aicuflow pipelines — including RAG pipelines and trained model endpoints — are designed to be consumed as APIs, making them compatible with MCP server wrappers as the ecosystem evolves.

→ See how Aicuflow's RAG pipeline works → Learn how trained models are deployed as APIs → Read about the broader AI assistant architecture