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Arclio Mcp Tooling

@Arclioon a year ago
2 MIT
FreeCommunity
AI Systems
Arclio MCP Tooling provides secure interfaces for AI models to interact with external services.

Overview

What is Arclio Mcp Tooling

Arclio MCP Tooling is a monorepo that contains a collection of Model Context Protocol (MCP) packages designed to enable AI models to securely interact with external services. It provides a standardized interface for AI assistants to access and manipulate data across various systems.

Use cases

Use cases for arclio-mcp-tooling include automating email management through Gmail, scheduling events via Google Calendar, managing files in Google Drive, and creating presentations with Google Slides, all within AI-driven applications.

How to use

To use arclio-mcp-tooling, ensure you have Python 3.10 or higher and Make installed. Clone the repository from GitHub, and follow the installation instructions provided in the README to set up the environment and start using the MCP packages.

Key features

Key features of arclio-mcp-tooling include secure integration with external services, a standardized interface for AI assistants, and specific packages like arclio-mcp-gsuite that facilitate operations with Google Workspace tools such as Drive, Gmail, Calendar, and Slides.

Where to use

Arclio MCP Tooling is primarily used in the field of AI application development, particularly for creating AI assistants that require secure access to external tools and services.

Content

Arclio MCP Tooling Monorepo

Model Context Protocol (MCP) tooling for AI assistants

Python 3.10+
License: MIT
Built with: UV

Developed and maintained by Arclio - Secure MCP service management for AI applications


📋 Overview

The arclio-mcp-tooling monorepo houses a collection of Model Context Protocol (MCP) packages that enable AI models to interact with external services securely. Each package in this repository provides a standardized interface for AI assistants to access and manipulate data in various systems.

What is MCP?

The Model Context Protocol (MCP) provides a standardized interface for AI models to access external tools and services. Each package in this monorepo implements an MCP server that exposes capabilities as tools that can be discovered and called by AI models.

🛠️ Packages

This monorepo contains the following packages:

📚 google-workspace-mcp

Google Workspace integration for AI assistants, providing tools for:

  • Google Drive (file operations)
  • Gmail (email management)
  • Google Calendar (event scheduling)
  • Google Slides (presentation creation)

🏗️ Architecture

The monorepo follows a standardized structure:

arclio-mcp-tooling/
├── packages/                    # Package implementations
│   └── google-workspace-mcp/    # Google Workspace integration
├── tests/                       # Testing infrastructure
├── Makefile                     # Build and development tasks
└── README.md                    # Documentation

📦 Getting Started

Prerequisites

  • Python 3.10 or higher
  • Make

Installation

# Clone the repository
git clone https://github.com/arclio/arclio-mcp-tooling.git
cd arclio-mcp-tooling

# Set up the development environment
make setup-dev

# Install all packages in development mode
make install-all

Running Tests

# Run all tests
make test

# Run only unit tests
make test-unit

# Run only integration tests
make test-integration

# Run tests for a specific package
make test google-workspace-mcp

Running Servers

# Run the Google Workspace MCP server
make run-google-workspace

🧩 Development

Adding Dependencies

# Add a dependency to the Google Workspace package
make add google-workspace-mcp google-api-python-client

Code Quality

# Run linting
make lint

# Fix linting issues
make fix

# Format code
make format

Building Packages

# Build all packages
make build

# Publish all packages
make publish

📄 License

This project is licensed under the MIT License - see the LICENSE file in each package for details.

🏢 About Arclio

Arclio is a leading provider of secure MCP service management for AI applications. We specialize in creating robust, enterprise-grade tools that enable AI models to interact with external services safely and effectively.

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