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.
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!
///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:
$ 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!
Once a developer, always a developer!
Java developer for many years, I have the joy of knowing JDK 1.1, JEE, Struts, ... and now Spring, Quarkus, (core, boot, batch), Angular, Groovy, Golang, ...
For more than ten years I was a Software Architect, a job that allowed me to face many problems inherent to the complex information systems in large groups.
I also had other lives, notably in automation and delivery with the implementation of CI/CD chains based on Jenkins pipelines.
I particularly like sharing and relationships with developers and I became a Developer Relation at OVHcloud.
This new adventure allows me to continue to use technologies that I like such as Kubernetes or AI for example but also to continue to learn and discover a lot of new things.
All the while keeping in mind one of my main motivations as a Developer Relation: making developers happy.
Always sharing, I am the co-creator of the TADx Meetup in Tours, allowing discovery and sharing around different tech topics.