What LLM model is used?
The examples connect to Ollama with the Qwen2.5-coder:14b model.
@thrkrdk
Basic information
Model Context Protocol: Client application with Langchain4j for spring boot ai mcp server
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
It is a Java-based MCP (Model Context Protocol) client/host built with Langchain4j, designed to connect to Spring Boot MCP servers. The server demonstrates integration with private LLMs like Ollama (Qwen2.5-coder:14b) and supports both SSE and STDIO transport modes. It is intended for developers building AI assistants that leverage MCP tools.
The project provides branches with step‑by‑step examples. The final branch (03‑use‑mcp‑server‑stadio) shows the complete setup. You run an MCP server demo image in podman in either SSE or STDIO mode, obtain the server URL (SSE) or STDIO transport, then create a ChatLanguageModel for Ollama, an MCP Transport, an MCP Client, a ToolProvider to fetch tools from the server, and finally an AI services instance (Bot) to call the assistant with a tool.
ChatLanguageModel and AI servicesThe examples connect to Ollama with the Qwen2.5-coder:14b model.
It supports both SSE (Server‑Sent Events) and STDIO (standard input/output) transport modes for MCP servers.
It acts as a **client/host** that connects to external Spring Boot MCP servers.
The setup requires a running Ollama instance and podman to host the MCP server demo image. Optional tools like ngrok/kaggle are used for external URLs in some branches.
The `03-use-mcp-server-stadio` branch is the final branch containing all the code.
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