MCP-Ollama-Client is a cross-platform command-line tool that simplifies interaction with Ollama’s local LLM models. It offers multi-turn conversation support, real-time streaming output, and customizable prompts. Users can seamlessly switch between models, manage conversation history, and integrate with scripts through its straightforward API wrapper. The client also supports token usage display and error handling, making local model experimentation and development more efficient and accessible.
MCP-Ollama-Client is a cross-platform command-line tool that simplifies interaction with Ollama’s local LLM models. It offers multi-turn conversation support, real-time streaming output, and customizable prompts. Users can seamlessly switch between models, manage conversation history, and integrate with scripts through its straightforward API wrapper. The client also supports token usage display and error handling, making local model experimentation and development more efficient and accessible.
MCP-Ollama-Client provides a unified interface to communicate with Ollama’s language models running locally. It supports full-duplex multi-turn dialogues with automatic history tracking, live streaming of completion tokens, and dynamic prompt templates. Developers can choose between installed models, customize hyperparameters like temperature and max tokens, and monitor usage metrics directly in the terminal. The client exposes a simple REST-like API wrapper for integration into automation scripts or local applications. With built-in error reporting and configuration management, it streamlines the development and testing of LLM-powered workflows without relying on external APIs.
Who will use MCP-Ollama-Client?
Developers
AI researchers
Hobbyists
Educators
AI enthusiasts
How to use the MCP-Ollama-Client?
Step1: Install MCP-Ollama-Client via pip or clone the repository.
Step2: Ensure Ollama is installed and running on your system.
Step3: Launch the client with mcp-ollama-client chat --model .
Step4: Enter prompts to start a multi-turn conversation.
Step5: Use --prompt-template to apply custom templates.
Step6: Integrate the REST-like API in your scripts for automation.