Demonstrates a client-server implementation using the Model Context Protocol (MCP) for connecting large language models with tools and data.
Provides an example implementation of a Model Context Protocol (MCP) server using Elixir and SSE transport.
Enables real-time tool execution and structured knowledge retrieval for AI clients using an MCP-based infrastructure.
Implements a Model Context Protocol server using HTTP Server-Sent Events (SSE) for real-time communication.
Serves live weather data and forecasts via REST and MCP APIs, optimized with built-in database caching to reduce external calls.
Demonstrates how to interact with Language Model (LLM) from MCP Servers using JBang, Quarkus, and Langchain4j.
Demonstrates how a single Model Context Protocol server can serve multiple LLM clients, decoupling tool execution from model selection.
Provides real-time weather information as callable tools for the Gemini CLI.
All results loaded