Uses Mermaid diagrams to visualize the architecture and data flow between AI tools and log sources
Distributed as a Python package through PyPI for easy installation and configuration
Built with Python and uses FastMCP framework for MCP server implementation
Supports TOML configuration format for Codex tool integration setup
Colab MCP 🔗
Stop losing context when you switch between AI coding tools.
A Model Context Protocol (MCP) server that lets Claude Code, Cursor, Codex, and other AI coding assistants share logs and session history with each other.
The Problem
You're coding with Claude Code. You make progress. Then you switch to Cursor to test something. Now you've lost all your context. You explain everything again. Then you jump to Codex. Explain it all over again.
It's exhausting.
The Solution
Colab MCP is a shared MCP server that exposes your chat logs, terminal history, and IDE events as tools and resources across all your AI coding assistants.
When you switch tools, your AI already knows what you were working on. No more copy-pasting. No more re-explaining. Just continuous flow.
✨ Features
🔄 Share context across tools - Claude Code, Cursor, Codex, Gemini
📜 Access chat transcripts from previous sessions
🔍 Search across all logs - find that conversation from last week
🎯 Session summaries - quick overview of what you were working on
🖥️ Terminal & IDE event tracking - see what commands were run
🚀 Fast setup - one command to install across all your tools
🚀 Quick Start
1. Install
2. Configure Your AI Tools
Run the interactive installer:
The installer will:
🔍 Detect which AI coding tools you have installed
✅ Let you choose which ones to configure
⚙️ Add Colab MCP to their MCP server configs
📝 Give you instructions to restart each tool
3. Restart Your AI Tools
Restart Claude Code, Cursor, Codex, or whichever tools you configured.
That's it! 🎉
📖 Usage
Once installed, Colab MCP exposes several tools and resources to your AI assistants:
Tools
list_sessions- Get a list of all coding sessionsfetch_transcript- Retrieve the full transcript of a sessionsummarize_session- Get a quick summary of what happenedsearch_logs- Search across all logs (chat, MCP, IDE events)codex_status- Check recent Codex CLI activity
Example Prompts
Try asking your AI assistant:
"What was I working on in my last session?"
"Search my logs for discussions about authentication"
"Summarize my session from yesterday afternoon"
"What errors did I encounter in the last hour?"
🛠️ Manual Configuration
If you prefer to configure manually, add this to your MCP config:
Claude Code (~/.claude/mcp.json)
Cursor (~/.cursor/mcp.json)
Codex (~/.codex/config.toml)
🗂️ Architecture
🤝 Contributing
Contributions are welcome! Check out the docs/ folder for more detailed information about how Colab MCP works.
📝 License
MIT License - see LICENSE for details.
🙏 Acknowledgments
Built with FastMCP - the fastest way to build MCP servers in Python.
Made with ❤️ by developers tired of losing context
This server cannot be installed
local-only server
The server can only run on the client's local machine because it depends on local resources.
Enables AI coding assistants like Claude Code, Cursor, and Codex to share chat logs, terminal history, and session context with each other. Eliminates the need to re-explain context when switching between different AI coding tools.