Model Context Protocol (MCP) with OVHcloud AI Endpoints

Stéphane Philippart27/06/20254 min read

If you want to have more information on AI Endpoints, please read the following blog post.
You can, also, have a look at our previous blog posts on how use AI Endpoints.

OVHcloud AI Endpoints allows developers to easily add AI features to there day to day developments.

In this article, we will explore how to create a Model Context Protocol (MCP) server and client using Quarkus and LangChain4J to interact with OVHcloud AI Endpoints.

ℹ️ You can find the full code on Github ℹ️

Introduction to Model Context Protocol (MCP)

In few words, MCP is a protocol that allows your LLM to ask for additional context or data from external sources during the generation processes.
⚠️ This is not the LLM that calls the external source, it's the client that handles the call and returns the result to the LLM. ⚠️

If you want more information about MCP, please refer to the official documentation.

In this blog post, we'll explore how to easily create, in Java, a MCP Server using Quarkus and a client using LangChain4J.

Creating a Server with Quarkus

The goal of this MCP server is to allow the LLM to ask for information about OVHcloud public cloud projects.

ℹ️ The code used to call the OVHcloud API is in the GitHub repository and will not be detailed here.

Thanks to Quarkus, the only things you need to create a MCP server is to define the tools that you want to expose to the LLM.

java

public class PublicCloudUserTool {

    @RestClient
    OVHcloudMe ovhcloudMe;

    @Tool(description = "Tool to manage the OVHcloud public cloud user.")
    ToolResponse getUserDetails() {
        Long ovhTimestamp = System.currentTimeMillis() / 1000;
        return ToolResponse.success(
                new TextContent(ovhcloudMe.getMe(OVHcloudSignatureHelper.signature("me", ovhTimestamp),
                        Long.toString(ovhTimestamp)).toString()));
    }
}

⚠️ The description is very important as it will be used by the LLM to choose the right tool for the task. ⚠️

At the time of writing, there are two type of MCP servers: stdio and Streamable HTTP.
This blog post uses the Streamable mode thanks to Quarkus with the quarkus-mcp-server-sse extension.

Run your server with the quarkus dev command. Your MCP server will be used on http://localhost:8080.

Using the MCP server with LangChain4J

You can, now, use the MCP server with LangChain4J to create a powerful chatbot that can now interact with your OVHcloud account!

java

///usr/bin/env jbang "$0" "$@" ; exit $?
//JAVA 24+
//PREVIEW
//DEPS dev.langchain4j:langchain4j-mcp:1.0.1-beta6 dev.langchain4j:langchain4j:1.0.1 dev.langchain4j:langchain4j-mistral-ai:1.0.1-beta6 


import dev.langchain4j.mcp.McpToolProvider;
import dev.langchain4j.mcp.client.DefaultMcpClient;
import dev.langchain4j.mcp.client.McpClient;
import dev.langchain4j.mcp.client.transport.McpTransport;
import dev.langchain4j.mcp.client.transport.http.HttpMcpTransport;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.mistralai.MistralAiChatModel;
import dev.langchain4j.service.AiServices;

// Simple chatbot definition with AI Services from LangChain4J
public interface Bot {
    String chat(String prompt);
}

void main() {
    // Mistral model from OVHcloud AI Endpoints
    ChatModel chatModel = MistralAiChatModel.builder()
            .apiKey(System.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN"))
            .baseUrl(System.getenv("OVH_AI_ENDPOINTS_MODEL_URL"))
            .modelName(System.getenv("OVH_AI_ENDPOINTS_MODEL_NAME"))
            .logRequests(false)
            .logResponses(false)
            .build();

    // Configure the MCP server to use
    McpTransport transport = new HttpMcpTransport.Builder()
            // https://xxxx/mcp/sse
            .sseUrl(System.getenv("MCP_SERVER_URL"))
            .logRequests(false)
            .logResponses(false)
            .build();

    // Create the MCP client for the given MCP server
    McpClient mcpClient = new DefaultMcpClient.Builder()
            .transport(transport)
            .build();

    // Configure the tools list for the LLM
    McpToolProvider toolProvider = McpToolProvider.builder()
            .mcpClients(mcpClient)
            .build();

    // Create the chatbot with the given LLM and tools list
    Bot bot = AiServices.builder(Bot.class)
            .chatModel(chatModel)
            .toolProvider(toolProvider)
            .build();

    // Play with the chatbot 🤖
    String response = bot.chat("Can I have some details about my OVHcloud account?");
    System.out.println("RESPONSE: " + response);

}

If you run the code you can see your MCP server and client in action:

bash

$ jbang SimpleMCPClient.java

DEBUG -- Connected to SSE channel at http://127.0.0.1:8080/mcp/sse
DEBUG -- Received the server's POST URL: http://127.0.0.1:8080/mcp/messages/ZDdkZTEyYWMtNzczMC00NDVkLWFhMjktZWI1MGI0YjVjNzFh
DEBUG -- MCP server capabilities: 
{"capabilities":
  {"resources":
    {"listChanged":true},
    "completions":{},
    "logging":{},
    "tools":
      {"listChanged":true},
      "prompts":
        {"listChanged":true}
  },
  "serverInfo":
    {"version":"1.0.0-SNAPSHOT",
    "name":"ovh-mcp"
    },
  "protocolVersion":"2024-11-05"
}

RESPONSE:  Here are the details for your OVHcloud account:
- First name: Stéphane
- Last name: Philippart
- City: XXX
- Country: FR
- Language: fr_FR

You can refer to these details when interacting with the OVHcloud platform or support.

You have a dedicated Discord channel (#ai-endpoints) on our Discord server (https://discord.gg/ovhcloud), see you there!

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