AI Context Manager MCP - Sync agent for AI assets (skills, prompts, specs, context)
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# AI Context Manager MCP Servidor MCP (Model Context Protocol) en Python que actúa como **sync agent** entre el workspace local y la aplicación cloud. ## ¿Qué hace? - Gestiona la carpeta `.ai/` en tu workspace (skills, prompts, specs, contexto, bootstrap) - Sincroniza assets con la app cloud (`cloud_sync pull/push`) - Genera `MODEL_BOOTSTRAP.md` adaptado al entorno (vscode, claude, opencode, cli, generic) - Mantiene `.gitignore` actualizado para no commitear el contexto local ## Requisitos - Python 3.11+ - pip / pipx ## Instalación ```bash # Con pipx (recomendado, instala en entorno aislado) pipx install . # O con pip en un virtualenv python -m venv .venv source .venv/bin/activate # Linux/macOS .venv\Scripts\activate # Windows pip install -e . ``` ## Variables de entorno (obligatorias para sync cloud) ```bash # Linux/macOS export AI_CONTEXT_MANAGER_BASE_URL="https://cloud.example.com" export AI_CONTEXT_MANAGER_TOKEN="pat_xxx" # Windows (PowerShell) $env:AI_CONTEXT_MANAGER_BASE_URL="https://cloud.example.com" $env:AI_CONTEXT_MANAGER_TOKEN="pat_xxx" # Windows (cmd) set AI_CONTEXT_MANAGER_BASE_URL=https://cloud.example.com set AI_CONTEXT_MANAGER_TOKEN=pat_xxx ``` ## Arrancar el servidor MCP ```bash # Modo stdio (para clientes MCP como Claude Desktop, OpenCode, etc.) python -m mcp_server # O usando el script instalado ai-context-manager serve ``` ## Configuración en VS Code (tasks.json) Crear `.vscode/tasks.json` en tu proyecto: ```json { "version": "2.0.0", "tasks": [ { "label": "AI Context Manager: Start MCP", "type": "shell", "command": "python -m mcp_server", "options": { "env": { "AI_CONTEXT_MANAGER_BASE_URL": "https://cloud.example.com", "AI_CONTEXT_MANAGER_TOKEN": "pat_xxx" } }, "problemMatcher": [] }, { "label": "AI Context Manager: Sync (pull)", "type": "shell", "command": "ai-context-manager cloud-sync --direction pull", "op…
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