Langchain4j MCP Host/Client for Spring Boot

Overview

The Langchain4J MCP Host/Client is a Spring‑Boot based server that bridges the Model Context Protocol (MCP) with the LangChain4J framework. It enables developers to expose AI‑powered tools, prompts, and sampling strategies as MCP services that Claude or other compatible assistants can consume. By turning a Spring application into an MCP endpoint, the server solves the common pain point of wiring together disparate AI components—LLMs, retrieval services, and custom logic—into a single, discoverable interface.

What the Server Does

At its core, the MCP host registers a ToolProvider that lists all available tools (e.g., language models, knowledge bases) and exposes them through standard MCP endpoints. Clients such as the LangChain4J client can then query these tools, invoke them via the MCP transport layer (SSE or STDIO), and receive structured responses. The server also supports dynamic configuration of underlying LLM backends (e.g., Ollama’s Qwen2.5‑coder) and can adapt to remote hosting platforms like Kaggle or ngrok, allowing seamless deployment in cloud or edge environments.

Key Features

• SSE and STDIO Transports – Two modes of communication are supported, enabling low‑latency streaming responses or traditional request/response flows.
• Tool Discovery and Invocation – The server publishes a catalog of tools that can be queried by name or function signature, making it easy for assistants to select the right capability at runtime.
• LLM Integration – Built‑in support for Ollama models means developers can plug in any compatible LLM without writing custom adapters.
• Spring Boot Ecosystem – Leveraging Spring’s dependency injection, configuration management, and actuator endpoints simplifies deployment and monitoring.
• Modular Branches – The repository’s branch structure demonstrates progressive use cases, from simple private LLM connections to full MCP server integration with SSE and STDIO.

Real‑World Use Cases

• Chatbot Backends – Deploy a conversational agent that can call external APIs or perform code generation via MCP‑exposed tools.
• Data Retrieval Pipelines – Combine LangChain4J’s retrieval mechanisms with MCP to let an assistant query a vector store or database on demand.
• Hybrid LLM Workflows – Route prompts to different models (e.g., a generalist vs. a code‑specialized model) through the MCP tool registry.
• Edge Deployment – Run the server in containers (e.g., Podman) with ngrok tunnels, making it accessible from remote assistants without exposing internal infrastructure.

Integration into AI Workflows

Developers can instantiate a LangChain4J pointing to the MCP server’s URL, then wrap it in an instance. The assistant can request tool execution by name; the MCP transport handles serialization, streaming, and error handling automatically. This tight coupling reduces boilerplate code, promotes reusability of AI components, and aligns with the MCP’s goal of decoupling assistants from specific tooling implementations.

Standout Advantages

• Seamless MCP Compatibility – The server speaks the same protocol that Claude expects, eliminating custom adapters.
• Rapid Prototyping – Branches in the repository provide ready‑to‑run examples for both SSE and STDIO, accelerating experimentation.
• Spring Boot Reliability – Built on a mature framework, the server inherits robust health checks, logging, and configuration patterns.

In summary, the Langchain4J MCP Host/Client empowers developers to expose sophisticated AI capabilities as standardized MCP services, streamlining the integration of LangChain4J models into modern AI assistants and enabling scalable, modular conversational architectures.