Ollama MCP Chat

by godstale

2 stars

193 downloads

Not rated

GitHub

About

Ollama MCP Chat is a desktop chatbot application that integrates Ollama's local LLM models with MCP (Model Context Protocol) servers

Details

Author: godstale
GitHub stars: 2
Downloads: 193
Categories: AI

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Ollama MCP Chat

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

From the repository

Clone the repository, install uv, run uv sync, install Ollama and pull a model (e.g., ollama pull qwen3:14b). Optionally configure MCP servers in mcp_config.json. Launch the GUI with uv run main.py. Type quit, exit, or bye to exit.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "ollama mcp chat": {
            "ollama-mcp-chat": {
                "command": "uv",
                "args": [
                    "sync"
                ]
            }
        }
    }
}

McpServers

{
    "ollama-mcp-chat": {
        "command": "uv",
        "args": [
            "sync"
        ]
    }
}

# Ollama MCP Chat Ollama MCP Chat is a desktop chatbot application that integrates Ollama's local LLM models with MCP (Model Context Protocol) servers, supporting various tool calls and extensible features. It provides a GUI based on Python and PySide6, and allows you to freely extend its capabilities via MCP servers. This project can be very useful as base code for developers who want to create AI applications with GUI in Python. ## Key Features - Run Ollama LLM models locally for free - Integrate and call various tools via MCP servers - Manage and save chat history - Real-time streaming responses and tool call results - Intuitive desktop GUI (PySide6-based) - GUI support for adding, editing, and removing MCP servers ## System Requirements - Python 3.12 or higher - [Ollama](https://ollama.ai) installed (for local LLM execution) - [uv](https://github.com/astral-sh/uv) (recommended for package management) - MCP server (can be implemented or use external MCP servers) - [smithery.ai](https://smithery.ai) (recommended for MCP repository) ## Installation 1. Clone the repository ```bash git clone https://github.com/your-repo/ollama-mcp-chat.git cd ollama-mcp-chat ``` 2. Install uv (if not installed) ```bash # Using pip pip install uv # Or using curl (Unix-like systems) curl -LsSf https://astral.sh/uv/install.sh | sh # Or using PowerShell (Windows) powershell -c "irm https://astral.sh/uv/install.ps1 | iex" ``` 3. Install dependencies ```bash # Install dependencies uv sync ``` 4. Install Ollama and download a model - recommend [qwen3:14b](ollama run qwen3:14b) ```bash # Install Ollama (see https://ollama.ai for details) ollama pull <model-name> ``` 5. MCP server configuration (optional) - Add MCP server information to the `mcp_config.json` file - Example: ```json { "mcpServers": { "weather": { "command": "python", "args": ["./mcp_server/mcp_server_weather.py"], "transport": "stdio" } } } ``` ## How to Run ```bash uv run main.py ``` - The GUI will launch, and you can start chatting and using MCP tools. ## Main Files - `ui/chat_window.py`: Main GUI window, handles chat/history/settings/server management - `agent/chat_history.py`: Manages and saves/loads chat history - `worker.py`: Handles asynchronous communication with LLM and MCP servers - `agent/llm_ollama.py`: Integrates Ollama LLM and MCP tools, handles streaming responses - `mcp_server/mcp_manager.py`: Manages and validates MCP server configuration files ## Extending MCP Servers 1. Add new MCP server information to `mcp_config.json` 2. Implement and prepare the MCP server executable 3. Restart the application and check the MCP server list in the GUI ## Chat History - All conversations are automatically saved to `chat_history.json` - You can load previous chats or start a new chat from the GUI ## Exit Commands - Type `quit`, `exit`, or `bye` in the program to exit ## Notes - Basic LLM chat works even without MCP server configuration - Be mindful of your PC's performance and memory usage, especially with large LLM models - MCP servers can be implemented in Python, Node.js, or other languages, and external MCP servers are also supported ## License MIT License

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