What is Mcp Cloud

mcp-cloud is a Model Context Protocol Server designed for public cloud environments such as AWS and Azure. It enables AI models to connect with various cloud resources and services, facilitating operations like CRUD on these resources.

Use cases

Use cases for mcp-cloud include managing cloud storage resources, integrating AI models with cloud services for data processing, and developing applications that require context-aware AI interactions.

How to use

To use mcp-cloud, set up the server in your public cloud environment, connect it to resources like S3 Buckets or Azure Blobs, and utilize the provided tools to perform operations on these resources. Refer to the MCP Quickstart Guide for detailed instructions.

Key features

Key features of mcp-cloud include seamless integration with public cloud resources, support for CRUD operations, and the ability to provide context to AI models through standardized protocols.

Where to use

mcp-cloud can be used in various fields including AI development, cloud computing, data management, and any application that requires interaction between AI models and cloud resources.

mcp-cloud

Model Context Protocol Server for Public Cloud environments like AWS, Azure, and more.
mcp-cloud is a Python MCP server:

• Connects to public cloud to access resources like S3 Buckets, Azure Blobs
• Provides tools performing certain operations like CRUD on resources.

NOTE: This a technology demonstrator only to be used with test data / account in cloud or with local LLM models like llama.

Overview

mcp-cloud is a Python server implementation of the Model Context Protocol (MCP) designed specifically for public cloud environments. It enables AI models to seamlessly connect with various cloud resources and services.

mcp-cloud

Model Context Protocol Server for Public Cloud
mcp-cloud is designed to run a MCP Server for Public Cloud like AWS, Azure etc.

Mode Context Protocol

MCP Protocol for GenAI agents has been created by Anthropic.
MCP is an open protocol that standardizes how applications provide context to LLMs.
Think of MCP like a USB-C port for AI applications. MCP provides a standardized way to connect AI models to different data sources and tools.
Refer Anthopic’s MCP Protocol Introduction.
Refer to the MCP Quickstart Guide for Users to learn how to use MCP

Concepts

MCP servers can provide three main types of capabilities:
Resources: File-like data that can be read by clients (like API responses or file contents)
Tools: Functions that can be called by the LLM (with user approval)
Prompts: Pre-written templates that help users accomplish specific tasks

Initial version of MCP Server will focus on resources available of Public Clouds e.g S3 Buckets, Azure for now.

MCP Primitives
The MCP protocol defines three core primitives that servers can implement:

Primitive Control Description Example Use
Prompts User-controlled Interactive templates invoked by user choice Slash commands, menu options
Resources Application-controlled Contextual data managed by the client application File contents, API responses
Tools Model-controlled Functions exposed to the LLM to take actions API calls, data updates

Running MCP Server

python src/main.py

or

uv run --with mcp mcp run main.py

Using mcp commands & mcp inspector

mcp install main.py 
mcp dev main.py

Features

0.1
[x] Cloud Storage
[ ] Cloud Compute
… Coming Soon …

Steps to Install

TODO: Update

Steps to Use

TODO:

Environment Setup

To set up your cloud storage credentials, you can use the provided environment setup script:

python src/set_env.py

This will:

1. Prompt you for your cloud provider credentials
2. Create a .env file with your settings
3. Verify that the environment variables are set correctly

The script will ask for:

• Cloud Provider (aws/azure/google)
• Access Key
• Secret Key
• Region (defaults to us-east-1)

Alternatively, you can manually create a .env file with the following variables:

CLOUD_PROVIDER=your_provider
CLOUD_ACCESS_KEY=your_access_key
CLOUD_SECRET_KEY=your_secret_key
CLOUD_REGION=your_region

Loading Environment Variables in System

Unix/Linux/MacOS

Using source command:

source .env

Or using export command:

export $(cat .env | xargs)

Windows

Command Prompt:

for /f "tokens=*" %a in (.env) do set %a

PowerShell:

Get-Content .env | ForEach-Object {
    if ($_ -match '^([^=]+)=(.*)$') {
        $name = $matches[1]
        $value = $matches[2]
        Set-Item -Path "Env:$name" -Value $value
    }
}

Verifying Environment Variables

• Unix/Linux/MacOS: printenv | grep CLOUD_
• Windows: set | findstr CLOUD_

Important Notes

1. The .env file should be in your project root directory
2. Each variable should be on a new line
3. No spaces around the = sign
4. No quotes around values unless they’re part of the value

Example .env file format:

CLOUD_PROVIDER=aws
CLOUD_ACCESS_KEY=your_access_key
CLOUD_SECRET_KEY=your_secret_key
CLOUD_REGION=us-east-1

Testing

The MCP Cloud Server includes comprehensive testing options to ensure everything is working correctly.

Quick Start Testing

# Run unit tests
python src/test_mcp_server.py

# Test with MCP Inspect
python src/main.py  # In one terminal
mcp-inspect info --url http://localhost:7008  # In another terminal

For detailed testing instructions, including how to test with Claude Desktop, see TESTING.md.

Testing with Claude Desktop

{
  "mcpServers": {
    "mcp-cloud": {
      "command": "<path to uv>/uv",
      "args": [
        "--directory",
        "<path_to_mcp_cloud>e/mcp_multi_cloud/src",
        "run",
        "--with",
        "mcp",
        "mcp",
        "run",
        "main.py"
      ],
      "env": {
        "CLOUD_PROVIDER": "aws",
        "CLOUD_ACCESS_KEY": "*******",
        "CLOUD_SECRET_KEY": "*******",
        "CLOUD_REGION": "us-east-1"
      }
    }
  }
}