What LLM does the project use?
It uses Llama3.2 via Ollama, and the model must support tool calling.
@oalles
Basic information
Showcases how to integrate Spring AI's support for MCP (Model Context Protocol) within Spring Boot applications, covering both server-side and client-side implementations.
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 integrates Spring AI’s support for the Model Context Protocol (MCP) within Spring Boot, demonstrating both server and client implementations. The project combines a Geocoder Service, a Timezone Service, and an MCP Host client that uses an LLM (Llama3.2 via Ollama) to answer geographical queries.
Run the three Spring Boot applications sequentially: Geocoder Service on port 8081, Timezone Service on port 8082, then MCP Host. Once started, enter city names in the console to receive latitude, longitude, and timezone details.
It uses Llama3.2 via Ollama, and the model must support tool calling.
Start the Geocoder Service on port 8081, the Timezone Service on port 8082, and then the MCP Host. All are Spring Boot apps run with `mvn spring-boot:run`.
MCP is a standard that streamlines management of contextual interactions in AI models, enabling consistent integration with external data sources and tools.
The project uses Spring AI MCP starters and the MCP Java SDK, plus Ollama for the LLM.
The README only mentions Llama3.2 via Ollama; a model supporting tool calls is required, but no other models are specified.

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