--- title: "AI Endpoints - Model Context Protocol (MCP) with LangChain4j" description: "Learn how to use Model Context Protocol (MCP) with Java, LangChain4j and OVHcloud AI Endpoints" url: https://docs.ovhcloud.com/en/guides/public-cloud/ai-machine-learning/ai-endpoints-mcp-langchain4j lang: en lastUpdated: 2025-08-06 --- # AI Endpoints - Model Context Protocol (MCP) with LangChain4j :::info AI Endpoints is covered by the **[OVHcloud AI Endpoints Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/48743bf-AI_Endpoints-ALL-1.1.pdf)** and the **[OVHcloud Public Cloud Special Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/d2a208c-Conditions_particulieres_OVH_Stack-WE-9.0.pdf)**. ::: ## Objective OVHcloud **[AI Endpoints](https://www.ovhcloud.com/en-gb/public-cloud/ai-endpoints/)** allows developers to easily add AI features to their day to day developments. In this article, we will explore how to create a Model Context Protocol (MCP) server and client using [Quarkus](https://quarkus.io/) and [LangChain4J](https://docs.langchain4j.dev/) to interact with OVHcloud AI Endpoints. Combined with OVHcloud **[AI Endpoints](https://www.ovhcloud.com/en-gb/public-cloud/ai-endpoints/)** which offers both LLM and embedding models, it becomes easy to create advanced, production-ready assistants. ![image](/images/public-cloud/ai-machine-learning/endpoints-tuto-15-mcp-langchain4j/robot.png) ## Definition - **Model Context Protocol**: MCP is a protocol that allows your LLM to ask for additional context or data from external sources during the generation processes. If you want more information about MCP, please refer to the [official documentation](https://modelcontextprotocol.io/introduction). - **[LangChain4j](https://github.com/langchain4j/langchain4j)**: Java-based framework inspired by [LangChain](https://github.com/langchain-ai/langchain), designed to simplify the integration of LLMs (Large Language Models) into applications. Note that LangChain4j is not officially maintained by the LangChain team, despite the similar name. - **[AI Endpoints](https://www.ovhcloud.com/en-gb/public-cloud/ai-endpoints/)**: A serverless platform by OVHcloud providing easy access to a variety of world-renowned AI models including Mistral, LLaMA, and more. This platform is designed to be simple, secure, and intuitive with data privacy as a top priority. ## Requirements - A [Public Cloud project](https://www.ovhcloud.com/en-gb/public-cloud/) in your OVHcloud account. - An access token for **OVHcloud AI Endpoints**. To create an API token, follow the instructions in the [AI Endpoints - Getting Started](/en/guides/public-cloud/ai-machine-learning/ai-endpoints-getting-started.md) guide. - This code example uses JBang, a Java-based tool for creating and running Java programs as scripts. For more information on JBang, please refer to the [JBang documentation](https://www.jbang.dev/documentation/guide/latest/installation.html). ## Instructions In this tutorial, we will 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. :::info ℹ️ The code used to call the [OVHcloud API](https://eu.api.ovh.com/) is in the [GitHub repository](https://github.com/ovh/public-cloud-examples/tree/main/ai/ai-endpoints/mcp-quarkus-langchain4j) and will not be detailed here. ::: Thanks to Quarkus, the only thing you need to create a MCP server, is to define the tools that you want to expose to the LLM. ```java // Quarkus code for MCP Server 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 types of MCP servers: [stdio](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#stdio) and [Streamable HTTP](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#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. ``` ## Conclusion In this article, we have seen how to create a Model Context Protocol (MCP) server and client using Quarkus and LangChain4J to interact with OVHcloud AI Endpoints. ## Go further You can find the full code example in the [GitHub repository](https://github.com/ovh/public-cloud-examples/tree/main/ai/ai-endpoints/mcp-quarkus-langchain4j). Browse the full [AI Endpoints documentation](/en/guides/public-cloud/ai-machine-learning/overview.md) to further understand the main concepts and get started. To discover how to build complete and powerful applications using AI Endpoints, explore our dedicated [AI Endpoints guides](/en/guides/public-cloud/ai-machine-learning/overview.md). If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/en-gb/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project. ## Feedback Please feel free to send us your questions, feedback, and suggestions regarding AI Endpoints and its features: - In the #ai-endpoints channel of the OVHcloud [Discord server](https://discord.gg/ovhcloud), where you can engage with the community and OVHcloud team members.