How do I use AI Context Manager?

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., "@AI Context Manager sync the latest assets from the cloud" 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.

AI Context Manager

by dannybombastic

Overview Schema Related Servers Score Discussions

Python

Hybrid

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

Related MCP server: MCP Project Standards Server

Requisitos

Python 3.11+
pip / pipx

Instalación

# 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)

# 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

# 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:

{
  "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",
      "options": {
        "env": {
          "AI_CONTEXT_MANAGER_BASE_URL": "https://cloud.example.com",
          "AI_CONTEXT_MANAGER_TOKEN": "pat_xxx"
        }
      },
      "problemMatcher": []
    }
  ]
}

Configuración en Claude Desktop

Añadir en claude_desktop_config.json:

{
  "mcpServers": {
    "ai-context-manager": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "env": {
        "AI_CONTEXT_MANAGER_BASE_URL": "https://cloud.example.com",
        "AI_CONTEXT_MANAGER_TOKEN": "pat_xxx"
      }
    }
  }
}

Configuración en OpenCode

Añadir en tu config de OpenCode:

{
  "mcp": {
    "servers": {
      "ai-context-manager": {
        "command": "python",
        "args": ["-m", "mcp_server"],
        "env": {
          "AI_CONTEXT_MANAGER_BASE_URL": "https://cloud.example.com",
          "AI_CONTEXT_MANAGER_TOKEN": "pat_xxx"
        }
      }
    }
  }
}

Setup inicial de un proyecto

# 1. Inicializar .ai/ en el workspace
ai-context-manager init --mode workspace

# 2. Vincular con proyecto cloud
ai-context-manager cloud-link --project-key my-project

# 3. Descargar assets del cloud
ai-context-manager cloud-sync --direction pull

# 4. Asegurar .gitignore
ai-context-manager ensure-gitignore

Tools disponibles (MCP)

Tool	Descripción
`init_storage`	Inicializa `.ai/` en workspace o global
`ensure_gitignore`	Añade `.ai/` al `.gitignore`
`scan_repo`	Escanea el repo buscando assets IA
`list_assets`	Lista assets (skills/prompts/specs/context)
`register_asset`	Registra un asset existente en el registry
`move_asset`	Mueve un asset actualizando el registry
`remove_asset`	Elimina un asset del registry
`create_skill`	Crea un nuevo skill desde template
`create_prompt`	Crea un nuevo prompt desde template
`create_spec`	Crea una nueva spec desde template
`generate_bootstrap`	Genera `MODEL_BOOTSTRAP.md` para el entorno
`cloud_project_link`	Vincula workspace con proyecto cloud
`cloud_sync`	Sincroniza assets (pull: cloud→local, push: local→cloud)
`cloud_pull_backup`	Descarga un backup específico del cloud

Resources disponibles (MCP)

Resource	Descripción
`registry://`	Contenido completo del registry.json
`context://bootstrap`	Contenido del MODEL_BOOTSTRAP.md
`skills://<id>`	Contenido de un skill por ID
`prompts://<id>`	Contenido de un prompt por ID
`specs://<id>`	Contenido de una spec por ID

Estructura local generada

.ai/
  registry.json          # fuente de verdad local
  context/
    AI_GUIDELINES.md
    MODEL_BOOTSTRAP.md   # generado por generate_bootstrap
  skills/
    *.md
  prompts/
    *.md
  specs/
    *.md
  templates/
    skill.md
    prompt.md
    spec.md
  .sync/
    state.json           # estado de sync (hashes/ETags)
    project.json         # binding local_path <-> project_key

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

1Releases (12mo)

Commit activity

Resources

GitHub Repository

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