🚀 MCP-Ollama Server

by Sethuram2003

26 stars

324 downloads

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GitHub

About

Extends Model Context Protocol (MCP) to local LLMs via Ollama, enabling Claude-like tool use (files, web, email, GitHub, AI images) while keeping data private. Modular Python servers for on-prem AI. #LocalAI #MCP #Ollama

Details

Author: Sethuram2003
GitHub stars: 26
Downloads: 324
Categories: AI

- 🔒 Complete data privacy – all computations run locally via Ollama
- 🔧 Tool use for local LLMs (file, calendar, and more)
- 🧩 Modular architecture with independent Python service modules
- 🔌 Easy integration via simple APIs
- 🚀 Performance optimized for responsive AI interactions
- 📦 Containerized deployment with Docker (coming soon)
- 🧪 Extensive test coverage for reliability

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 🚀 MCP-Ollama Server

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": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

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": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

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": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

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": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

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": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

From the repository

Install prerequisites (Python 3.8+, Ollama, Git), clone the repository, and pull a model (e.g., ollama pull llama3). Then navigate to a module directory (e.g., client_mcp/) and run uv run client.py ../file_system/file_system.py to start interacting with the agent.

Claude Desktop / Cursor

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

{
    "mcpServers": {
        "\ud83d\ude80 mcp-ollama server": {
            "MCP-ollama_server": {
                "command": "uv",
                "args": [
                    "run",
                    "client.py",
                    "../file_system/file_system.py"
                ]
            }
        }
    }
}

McpServers

{
    "MCP-ollama_server": {
        "command": "uv",
        "args": [
            "run",
            "client.py",
            "../file_system/file_system.py"
        ]
    }
}

🚀 MCP-Ollama Server

Connect the power of Model Context Protocol with local LLMs

Getting Started •
Features •
Architecture •
Documentation •
Contributing •
FAQ

</div>

📋 Overview

MCP-Ollama Server bridges the gap between Anthropic's Model Context Protocol (MCP) and local LLMs via Ollama. This integration empowers your on-premise AI models with Claude-like tool capabilities, including file system access, calendar integration, web browsing, email communication, GitHub interactions, and AI image generation—all while maintaining complete data privacy.

Unlike cloud-based AI solutions, MCP-Ollama Server:
- Keeps all data processing on your local infrastructure
- Eliminates the need to share sensitive information with third parties
- Provides a modular approach that allows you to use only the components you need
- Enables enterprise-grade AI capabilities in air-gapped or high-security environments

✨ Key Features

- 🔒 Complete Data Privacy: All computations happen locally through Ollama
- 🔧 Tool Use for Local LLMs: Extends Ollama models with file, calendar, and other capabilities
- 🧩 Modular Architecture: Independent Python service modules that can be deployed selectively
- 🔌 Easy Integration: Simple APIs to connect with existing applications
- 🚀 Performance Optimized: Minimal overhead to maintain responsive AI interactions
- 📦 Containerized Deployment: Docker support for each module (coming soon)
- 🧪 Extensive Testing: Comprehensive test coverage for reliability

🚀 Quick Start

Prerequisites

- Python 3.8+ installed
- Ollama set up on your system
- Git for cloning the repository

🧩 Component Overview

MCP-Ollama Server is organized into specialized modules, each providing specific functionality:

📅 Calendar Module

calendar/
├── README.md          # Module-specific documentation
├── google_calendar.py # Google Calendar API integration
├── pyproject.toml     # Dependencies and package info
└── uv.lock        # Dependency lock file

The Calendar module enables your local LLM to:
- Create, modify, and delete calendar events
- Check availability and scheduling conflicts
- Send meeting invitations
- Set reminders and notifications

🔄 Client MCP Module

client_mcp/
├── README.md      # Module-specific documentation
├── client.py      # Main client implementation
├── pyproject.toml # Dependencies and package info
├── testing.txt    # Test data
└── uv.lock        # Dependency lock file

The Client module provides:
- A unified interface to interact with all MCP-enabled services
- Conversation history management
- Context handling for improved responses
- Tool selection and routing logic

📁 File System Module

file_system/
├── README.md          # Module-specific documentation
├── file_system.py     # File system operations implementation
├── pyproject.toml     # Dependencies and package info
└── uv.lock            # Dependency lock file

The File System module allows your local LLM to:
- Read and write files securely
- List directory contents
- Search for files matching specific patterns
- Parse different file formats (text, CSV, JSON, etc.)

Installation

```bash

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About

Details

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

🚀 MCP-Ollama Server

📋 Overview

✨ Key Features

🚀 Quick Start

Prerequisites

🧩 Component Overview

📅 Calendar Module

🔄 Client MCP Module

📁 File System Module

Installation

Install

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