Ollama MCP Chat

Ollama MCP Chat

Ollama MCP Chat is a desktop chatbot framework that marries Ollama’s fast, locally‑hosted language models with the Model Context Protocol (MCP). By running LLMs on a user’s own machine, it eliminates the need for cloud APIs and associated latency or cost. At the same time, MCP enables the chatbot to invoke external tools—such as weather lookups, data analysis scripts, or custom APIs—without hard‑coding those capabilities into the core application. This dual design gives developers a flexible, privacy‑first platform for building AI assistants that can both reason and act.

The application is built on Python with a PySide6 GUI, making it immediately usable for developers familiar with desktop UI development. It offers a full chat interface that supports real‑time streaming responses, tool‑call results, and persistent conversation history. Users can add, edit, or remove MCP servers directly from the GUI, allowing quick experimentation with new tools or services. The chat history is automatically saved to a JSON file, so previous sessions can be reloaded or analyzed later. Because the LLM runs locally, developers can freely tweak model parameters, swap in different Ollama models (e.g., qwen3:14b), or even deploy the app on low‑resource devices.

Key capabilities include:

• Local LLM execution: Run any Ollama model on the client machine, ensuring low latency and no external data exposure.
• MCP tool integration: Call arbitrary tools defined by MCP servers, enabling the assistant to fetch real‑time data or perform computations on demand.
• Streaming and tool‑call handling: Display responses as they arrive, including intermediate tool call outputs, giving a natural conversational feel.
• Extensibility: Add new MCP servers by editing a JSON config; the application automatically discovers and validates them.
• Developer-friendly architecture: The codebase is organized into clear modules (UI, worker, LLM integration, MCP manager), making it straightforward to fork or extend.

Real‑world scenarios that benefit from this setup include:

• Customer support bots that can query internal knowledge bases or ticketing systems via MCP servers while answering queries with a local LLM.
• Data‑analysis assistants that invoke Python scripts or R notebooks as tools, returning results within the chat.
• Privacy‑centric applications where all data stays on the user’s machine, such as personal finance planners or health trackers.

By combining local LLM inference with the modularity of MCP, Ollama MCP Chat gives developers a powerful yet simple foundation for building intelligent desktop assistants that can reason, act, and grow with new tools over time.