Everything You Need to Know About MCP (Part 2)

Can MCP Replace RAG?

MCP does not replace RAG (Retrieval Augmented Generation). Instead, it complements it.

RAG works best when an AI model needs access to large, mostly static collections of unstructured data such as documents, manuals, or web pages. It retrieves relevant text chunks from an index, embeds them into the model’s context, and then generates a response.

MCP, on the other hand, is designed for dynamic and structured data like real time APIs, databases, CRMs, or analytics dashboards.

Rather than searching pre indexed text, MCP allows the model to call external tools or data sources live and receive fresh, structured responses.

In short, RAG retrieves knowledge while MCP executes actions.

In modern architectures, both are often used together. RAG provides historical or reference context, while MCP enables real time interaction.

Can MCP Replace APIs?

MCP does not replace APIs. It builds on top of them.

Traditional APIs such as REST or GraphQL expose endpoints for developers to interact with services. MCP standardizes how AI agents interact with those same APIs.

You can think of MCP as an intelligent layer above APIs that makes them understandable and callable by AI models.

Instead of writing custom wrappers or prompts for each API, developers expose their service as an MCP server. The AI model can then automatically discover and call those tools using standard methods like tools/list and tools/call.

In summary:

• APIs provide the data and functionality
• MCP standardizes how AI models call those APIs

MCP is not a replacement for APIs. It is a bridge between APIs and AI.

Can MCP Call Another MCP?

Yes, MCP servers can call other MCP servers.

The protocol supports chaining and composition, allowing one MCP endpoint to forward requests to another. This enables multi layer or recursive communication between agents and tools.

For example:

• One MCP server handles authentication and logging
• Another MCP server fetches real time analytics
• A parent MCP server combines both responses and returns the result to the AI model

This composability makes MCP well suited for complex AI ecosystems that rely on multiple data sources or actions.

Can MCP Only Be Used with Claude?

No, MCP is not exclusive to Claude.

Although MCP was developed by Anthropic and Claude was the first model with native support, MCP is an open and model agnostic protocol.

Any large language model including GPT, Gemini, Llama, or Mistral can use MCP as long as the client application implements the protocol.

This means the same MCP server can serve multiple AI models without modification, which has helped MCP emerge as a cross vendor standard for AI tool interoperability.

Can MCP Work with OpenAI?

Yes, MCP works seamlessly with OpenAI.

OpenAI’s Agents SDK includes built in support for MCP. Developers can connect an MCP server directly to GPT models using classes such as HostedMCPTool or MCPServerStreamableHttp.

Example configuration:

HostedMCPTool({
  type: "mcp",
  server_label: "myserver",
  server_url: "https://example.com/mcp",
  require_approval: "never"
})

This setup allows an OpenAI agent to automatically discover and use all tools exposed by the MCP server.

Additionally, projects like FastMCP can convert existing REST APIs into MCP servers, enabling instant compatibility with both Claude and OpenAI GPT models.

In short, MCP integration with OpenAI is production ready and fully supported.