Extending a language model’s memory can be done in different ways.Model Composition Protocol (MCP) servers and retrieval‑augmented generation (RAG) both augment a model’s context but with different trade‑offs. This post explains what MCP and RAG are and when to use each for your AI applications.

Table of Contents

• 1. Understanding MCP Servers
• 2. Understanding Retrieval Augmented Generation
• 3. Comparing Architecture and Use Cases
• 4. Choosing the Right Approach for Your AI
• 5. Conclusion

1. Understanding MCP Servers

MCP stands for Model Composition Protocol. An MCP server allows language models to query structured data sources—like databases, APIs or live metrics—during inference. Instead of embedding all knowledge into the model itself, the model asks the MCP server for specific information via a standard protocol. The server executes the query, returns structured results and the model incorporates them into its response.

MCP is well suited for dynamic data that changes frequently, such as stock prices, sensor readings or customer records. Because queries happen on the fly, responses reflect the current state of the world. Security and permission checks happen at the API level, ensuring that the model does not access unauthorised data.

2. Understanding Retrieval Augmented Generation

RAG, described earlier, retrieves unstructured documents from a vector store based on similarity to the query. It works best with static content like manuals, policies or documentation. You control what the model sees by curating the knowledge base. The retrieval step adds latency but does not require model retraining. The model uses the additional context to answer questions more accurately.

3. Comparing Architecture and Use Cases

MCP and RAG both augment a model’s context, but they differ in the type of data and how it is retrieved. MCP deals with live, structured data via APIs. RAG deals with static, unstructured text via a vector search. MCP queries are deterministic and must be precise; RAG uses semantic similarity to find relevant passages. In terms of latency, MCP can be slower if the external API is slow; RAG adds a database lookup but can be cached.

For example, an MCP call might ask, “What is the current temperature in Amsterdam?” and the server queries a weather API. A RAG call might ask, “Explain the process for issuing a passport,” and retrieve paragraphs from a government manual. The former requires real‑time accuracy; the latter benefits from curated documents.

4. Choosing the Right Approach for Your AI

Use MCP when you need up‑to‑date, structured information and can integrate with existing APIs securely. MCP is ideal for analytics, personalisation and business workflows. Use RAG when your data is mostly static, unstructured and too large to embed in a model. You can combine both: an AI assistant could query an MCP server for real‑time metrics and use RAG for background knowledge. Ultimately the decision depends on your latency budget, data type and security requirements.

5. Conclusion

MCP servers and RAG are complementary techniques for extending a language model’s knowledge. MCP connects models to live structured data via APIs, while RAG attaches unstructured documents via vector search. Understanding their differences helps you architect AI systems that deliver accurate, timely and secure responses.