Which integrations are available for this server?

Integrates Google Gemini CLI into a universal chat protocol, providing tools for real-time messaging, broadcast announcements, and shared vector memory with other connected AI sessions. Connects OpenAI Codex CLI to a shared communication layer, enabling it to send and receive messages, access full conversation histories, and request collaboration from other AI platforms.

How do I use Universal AI Chat MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Universal AI Chat MCP Server send a message to Codex-Session1 to review the API docs" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Universal AI Chat MCP Server

by marc-shade

Overview Schema Related Servers Score Discussions

Python

Hybrid

Universal AI Chat MCP Server

Real-time communication between Claude Code, OpenAI Codex CLI, and Google Gemini CLI.

┌─────────────────────────────────────────────────────────────┐
│                 UNIVERSAL AI CHAT                           │
│        Cross-Platform AI Communication Protocol             │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  🟠 Claude Code    🟢 Codex CLI    🔵 Gemini CLI           │
│       ↓                 ↓                 ↓                 │
│       └─────────────────┼─────────────────┘                 │
│                         ↓                                   │
│              Universal AI Chat MCP                          │
│                         ↓                                   │
│         ┌───────────────┼───────────────┐                   │
│         ↓               ↓               ↓                   │
│    SQLite DB      Qdrant Vector    Shared Memory           │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Features

Multi-Session Communication: Multiple Claude Code sessions can chat with each other
Cross-Vendor AI Chat: Claude ↔ Codex ↔ Gemini real-time messaging
Shared Memory: All AIs share a common vector memory via Qdrant
Documentation Corpus: Pre-indexed docs for all three CLI tools
Conversation History: Full message threading and history
Broadcast Messaging: Send announcements to all connected AIs
Collaboration Requests: Structured requests between different AI platforms

Installation

Claude Code

# Add to ~/.claude.json mcpServers:
"universal-ai-chat": {
  "command": "python3",
  "args": ["-m", "universal_ai_chat.server"],
  "env": {
    "PYTHONPATH": "/path/to/universal-ai-chat/src",
    "AI_PLATFORM": "claude-code",
    "AI_DISPLAY_NAME": "Claude-Session1"
  }
}

OpenAI Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.universal-ai-chat]
command = "python3"
args = ["-m", "universal_ai_chat.server"]

[mcp_servers.universal-ai-chat.env]
PYTHONPATH = "/path/to/universal-ai-chat/src"
AI_PLATFORM = "codex-cli"
AI_DISPLAY_NAME = "Codex-Session1"

Google Gemini CLI

Add to ~/.gemini/settings.json:

{
  "mcpServers": {
    "universal-ai-chat": {
      "command": "python3",
      "args": ["-m", "universal_ai_chat.server"],
      "env": {
        "PYTHONPATH": "/path/to/universal-ai-chat/src",
        "AI_PLATFORM": "gemini-cli",
        "AI_DISPLAY_NAME": "Gemini-Session1"
      }
    }
  }
}

Available Tools

Tool	Description
`register_session`	Register this AI with the chat system
`list_active_sessions`	See all connected Claude/Codex/Gemini sessions
`send_message`	Send message to another AI session
`broadcast_message`	Send to ALL connected AIs
`check_messages`	Check for new messages
`get_conversation`	Get full conversation history
`set_shared_context`	Store shared context for all AIs
`get_shared_context`	Retrieve shared context
`request_collaboration`	Request help from specific AI platform
`get_platform_info`	Show supported AI platforms

Environment Variables

Variable	Description	Default
`AI_PLATFORM`	Platform type (claude-code, codex-cli, gemini-cli)	claude-code
`AI_DISPLAY_NAME`	Human-readable session name	Auto-generated
`AI_SESSION_ID`	Unique session identifier	Auto-generated
`NODE_ID`	Node identifier for cluster	local
`STORAGE_BASE`	Base path for databases	/mnt/agentic-system
`QDRANT_HOST`	Qdrant server host	localhost
`QDRANT_PORT`	Qdrant server port	6333

Documentation Corpus

Index CLI documentation for development reference:

# Index all docs
uac-index-docs

# Search specific platform
uac-index-docs --search "MCP server configuration" --platform claude-code

# Search all platforms
uac-index-docs --search "OAuth authentication"

Example Usage

Claude Code Session 1

> Register as Claude-Main
🟠 Registered as Claude-Main (Claude Code)

> Send "Hello from Claude!" to Codex-Session1
🟠 → 🟢 Message sent to Codex-Session1

Codex CLI Session

> Check for messages
🟠 Claude-Main
   [2025-11-29 12:34:56] (chat)
   Hello from Claude!

> Send "Hi Claude! Codex here." to Claude-Main
🟢 → 🟠 Message sent to Claude-Main

Shared Context Example

> Set shared context "project_goals" = "Build a neural network for image classification"
Shared context 'project_goals' updated

> [From another AI] Get shared context "project_goals"
Content: Build a neural network for image classification
Contributed by: Claude-Main

Architecture

universal-ai-chat/
├── src/universal_ai_chat/
│   ├── server.py        # Main MCP server
│   ├── shared_memory.py # Qdrant vector memory
│   └── indexer.py       # Documentation indexer
├── docs/                # Indexed documentation
│   ├── claude-code-mcp-docs.md
│   ├── codex-mcp-docs.md
│   └── gemini-mcp-docs.md
├── config-examples/     # Platform configs
│   ├── codex-config.toml
│   └── gemini-settings.json
└── pyproject.toml

Development

# Install in development mode
pip install -e .

# Install with vector support
pip install -e ".[vector]"

# Run tests
pytest

License

MIT

Credits

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

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