vibegit-mcp

# VibeGit MCP Server A Model Context Protocol (MCP) server for logging and analyzing AI assistant conversations. ## Prerequisites You need only two steps to get started: ### Step 1: Installation ```bash pip install vibegit-mcp ``` ### Step 2: Configuration Once installed, you can configure the MCP configuration file to enable the VibeGit MCP server. Assuming you are using VSCode, you can add a `mcp.json` file in the `.vscode/` directory of your project with the following content: ```json { "servers": { "vibegit": { "type": "stdio", "command": "vibegit-mcp" } } } ``` ## Usage After configuring the MCP server, you can start your AI Coding Agent in VSCode. The VibeGit MCP server will automatically log all conversation rounds to the `.vibe/` directory in your project root. ## Features - Log complete conversation rounds between users and AI assistants - Track file operations and tool usage All the logs and data are stored in the `.vibe/` directory under the project root. The directory structure is as follows: ``` .vibe/ ├── rounds/ │ ├── 2023-03/ │ │ ├── round-1.json │ │ ├── round-2.json │ ├── 2023-04/ │ │ ├── round-3.json │ │ ├── round-4.json ├── index.jsonl ├── sessions/ │ ├── session-1.json │ ├── session-2.json ``` Each `round-*.json` file contains detailed information about a single conversation round, including user inputs, AI responses, and any file operations and tool usage performed. The `index.jsonl` file provides a quick reference to all rounds, and the `sessions/` directory contains session metadata. Each session contains the consecutive rounds of conversations. ## Building and Publishing (For Maintainers) This package uses modern Python packaging with `pyproject.toml`. #### Prerequisites Install build tools: ```bash pip install build twine ``` Set up PyPI credentials in `~/.pypirc`: ```ini [distutils] index-servers = p…