Colab MCP
Colab MCP is a model context protocol server that allows AI programming assistants such as Claude Code, Cursor, and Codex to share logs and session histories, solving the problem of losing context when switching between different AI tools.
rating : 2.5 points
downloads : 4.4K
What is Colab MCP?
Colab MCP is a shared model context protocol server designed specifically to solve the context switching problem between AI programming assistants. It allows different AI programming tools (such as Claude Code, Cursor, Codex, etc.) to share chat logs, terminal histories, and IDE events, ensuring that you won't lose your work context when switching tools.How to use Colab MCP?
Using Colab MCP is very simple: After installing the Python package, run the interactive installer, which will automatically detect and configure the AI programming tools you have installed. After the configuration is complete, restart your AI tools to start enjoying the seamless context sharing experience.Use cases
Colab MCP is particularly suitable for developers who need to frequently switch between multiple AI programming tools. For example, writing code in Claude Code, testing functions in Cursor, and debugging problems in Codex. It is also suitable for team collaboration, allowing the AI tools used by different members to share project contexts.Main features
Cross - tool context sharing
Allows AI programming assistants such as Claude Code, Cursor, Codex, and Gemini to share work contexts, eliminating information gaps caused by tool switching.
Chat record access
Access the complete chat records of all previous sessions, so that the AI assistant can understand the content and decision - making process of previous discussions.
Cross - log search
Perform a full - text search in all log files (chat records, MCP logs, IDE events) to quickly find the information you need.
Session summary
Automatically generate a brief summary of the work session to quickly understand the previous work content and progress.
Terminal and IDE event tracking
Record and analyze the terminal command execution history and IDE operation events to fully restore the development process.
Quick installation and configuration
One - click installation and automatic configuration, supporting the detection of installed AI tools and automatic completion of MCP server configuration.
Advantages
Seamless context switching: Maintain work continuity when switching between different AI programming tools
Improve work efficiency: Reduce the time for repeated explanations and context reconstruction
Complete historical records: Keep complete records of all development sessions for easy backtracking
Easy to install and use: Automated configuration process, no need for users to manually edit configuration files
Cross - platform compatibility: Support mainstream AI programming tools and operating systems
Limitations
Tool support dependency: AI tools need to support the MCP protocol to work properly
Log file access: Need to read the log files of each tool, which may involve file permission settings
Storage space: Long - term use will accumulate a large amount of log data, which needs to be cleaned regularly
Configuration complexity: Some knowledge of the MCP configuration of each tool is required for manual configuration
How to use
Install Colab MCP
Install the Colab MCP Python package via pip
Run the interactive installer
Execute the installer, which will automatically detect the AI programming tools you have installed and provide configuration options
Select the tools to configure
Select the AI programming tools you want to enable Colab MCP for in the installer interface
Restart AI tools
Restart the corresponding AI tools according to the installer's instructions to make the configuration take effect
Usage examples
Continue previous work
After starting a project in Claude Code, switch to Cursor to continue development. The AI assistant can immediately understand the project background and previous discussions.
Search for technical discussions
Search for previous discussion records about specific technical issues (such as authentication implementation).
Session review
Quickly understand the work content and progress of yesterday or a specific time period.
Error troubleshooting
Find the latest error information and solutions.
Frequently Asked Questions
Which AI programming tools does Colab MCP support?
Do I need to manually configure after installation?
Will Colab MCP store my chat records?
If I have multiple projects, can Colab MCP distinguish them?
How to manually configure Colab MCP?
Related resources
Official documentation
Complete documentation and detailed configuration instructions for Colab MCP
FastMCP project
The FastMCP framework on which Colab MCP is based, used for quickly building MCP servers
MCP protocol specification
Official specification and documentation of the Model Context Protocol
Problem feedback
Report problems, submit feature requests, or participate in discussions

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