cloud-chat-assistant

Multi-cloud MCP server — talk to models on Azure AI Foundry, AWS Bedrock, and Google Vertex AI from any AI CLI agent.

What it does

Exposes cloud AI models as MCP tools so AI CLI agents (Claude Code, Gemini CLI, Copilot CLI) can query them programmatically. Supports streaming, conversation history, parallel multi-model queries, and dynamic model discovery via CLIs.

Related MCP server: Multi-LLM Gateway MCP

Supported Providers

Quick start

Prerequisites

• Python 3.8+

• At least one cloud provider configured

Install

git clone https://github.com/techempower-org/cloud-chat-assistant.git
cd cloud-chat-assistant
python3 -m venv venv
./venv/bin/pip install httpx

Configure

The server auto-creates ~/.config/cloud-chat-assistant/ on first run.

Environment variables (recommended):

# Azure AI Foundry
export AZURE_AI_API_KEY="your-azure-key"
export AZURE_AI_ENDPOINT="https://your-resource.services.ai.azure.com"

# AWS Bedrock
export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_DEFAULT_REGION="us-east-1"

# Google Vertex AI
export GOOGLE_API_KEY="your-vertex-ai-key"
export GOOGLE_PROJECT="your-gcp-project-id"
export GOOGLE_REGION="global"

Or config file (~/.config/cloud-chat-assistant/config.json):

{
    "api_key": "your-azure-key",
    "endpoint": "https://your-resource.services.ai.azure.com",
    "deployment": "gpt-5.3-chat",
    "model_type": "deployed",
    "aws_access_key": "your-access-key",
    "aws_secret_key": "your-secret-key",
    "aws_region": "us-east-1",
    "google_api_key": "your-vertex-ai-key",
    "google_project": "your-gcp-project-id"
}

Register with your CLI agent

Claude Code — add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "cloud-chat": {
      "command": "python3",
      "args": ["/path/to/cloud-chat-assistant/mcp_cloud_chat.py"]
    }
  }
}

Gemini CLI — add to ~/.gemini/settings.json under mcpServers.

Copilot CLI — add to ~/.copilot/mcp.json under mcpServers.

Usage Examples

Switch providers

configure(model_type="bedrock", deployment="claude-opus-4.6")
configure(model_type="deployed", deployment="gpt-5.3-chat")
configure(model_type="serverless", deployment="Meta-Llama-3.1-405B-Instruct")

Multi-model queries

multi_chat(message="Explain quantum entanglement", models=["gpt-5.3-chat", "claude-opus-4.6", "gemini-3.1-pro-preview"])

Scan all providers

scan()

Returns a matrix showing which models are working, unavailable, or deployable.

CLI Integration (Optional)

Install cloud CLIs for dynamic model discovery:

# Azure — list deployable models
curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash
az login

# AWS — list Bedrock models
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip && sudo ./aws/install
aws configure

# Google — auth tokens for Vertex AI
sudo apt install google-cloud-cli
gcloud auth login

See CLI_SETUP.md for detailed instructions.

Ecosystem

This project is part of a four-project voice AI system:

Voice Integration

Pair with speech-to-cli for voice conversations:

1. multi_chat — queries all models in parallel

2. multi_speak — synthesizes all responses, plays sequentially

GNOME Speaks Integration

gnome-speaks can call cloud-chat-assistant directly for AI conversation mode, and its preferences panel can configure this project's settings (~/.config/cloud-chat-assistant/config.json) — including provider credentials, generation parameters, and model selection — from a unified GNOME settings UI.

Architecture

• Async: asyncio + httpx with connection pooling

• Streaming: SSE with producer-consumer queue

• Protocol: MCP v2024-11-05 over stdio, JSON-RPC 2.0

• Config: Auto-migrates from old azure-chat-assistant location

License

GPLv3 — see LICENSE.