semvec/pypi-packages huggingface dataset:
ctx-engine-mcp 0.1.1
A Model Context Protocol (MCP) server for Context Engineering - file system as external memory for AI agents
License: MIT
Requires Python: >=3.10
Keywords: agent, ai, claude, context-engineering, llm, mcp

# Context Engineering MCP Server

这是一个符合 **Model Context Protocol (MCP)** 标准的服务器，旨在为 Claude Desktop、Cursor 或其他 AI 助手提供"外部记忆"和"上下文管理"能力。

它实现了 [Context Engineering](./ai-programming-best-practices.md) 的核心理念，允许 AI 像操作系统一样管理自己的工作区状态。

## ✨ 功能

这个插件为你的 AI 助手添加了以下能力（Tools）：

1.  **`context_init`**: 初始化 `.ai/skills` 和 `.agent_memory` 目录结构。
2.  **`context_state`**: 获取当前工作区的"快照"（包含目录树、Git状态、当前目标）。
3.  **`context_save_observation`**: 将长输出（如日志、API响应）保存到文件，只返回摘要。
4.  **`context_read_observation`**: 按需读取之前保存的观察结果。

## 📦 安装与配置

### 1. 作为 Python 包安装

```bash
# 从 PyPI 安装
pip install ctx-engine-mcp
```

### 2. 配置 Claude Desktop

编辑你的 Claude Desktop 配置文件（通常位于 `~/Library/Application Support/Claude/claude_desktop_config.json`）：

```json
{
  "mcpServers": {
    "context-engineering": {
      "command": "uv",
      "args": [
        "tool",
        "run",
        "--from",
        "ctx-engine-mcp",
        "context-mcp"
      ]
    }
  }
}
```

或者使用系统 Python：

```json
{
  "mcpServers": {
    "context-engineering": {
      "command": "python3",
      "args": ["-m", "context_mcp.server"]
    }
  }
}
```

### 3. 配置 Cursor (即将支持)

Cursor 目前正在积极集成 MCP，配置方式与 Claude Desktop 类似。

## 🛠️ 使用示例

一旦配置完成，你可以在对话中直接告诉 Claude：

> "初始化一下上下文环境" -> Claude 会调用 `context_init`

> "现在的项目状态是什么？" -> Claude 会调用 `context_state`

> "运行 npm install 并帮我分析错误，输出可能很长，请注意使用外部记忆" -> Claude 会运行命令并使用 `context_save_observation` 保存结果。
aind-watchdog-service 0.1.6
Generated from aind-library-template
License: MIT
Requires Python: >=3.10

# aind_watchdog_service

## Usage

Watchdog runs in the background (see [deployment](#deployment)), and watches a directory for newly-created yaml files containing instructions to transfer files.

On a computer where the watchdog service is running, manifests can be submitted by creating a file in the right folder. To do this in your software, `pip install aind-watchdog-service` and use the `aind_watchdog_service.models.ManifestConfig` object to write your manifest file in the proper format. See the examples/ folder in this repo for more details.

## Configuration

Watchdog is configured using SIPE's configuration zookeeper server. The configuration options are listed in /src/aind_watchdog_service/models/watch_config.py.

## Monitoring

Watchdog logs can be monitored from the main SIPE logserver (http://eng-logtools:8080/?channel_filter=watchdog&hide=location,count) or this [grafana dashboard](http://eng-tools/grafana/d/de377sfsa9fcwf/watchdog-service-logs?var-acquisition_age=7d&orgId=2&from=now-7d&to=now&timezone=browser&var-hostname=$__all&refresh=auto).

## Deployment

Deployment in the institute is handled by SIPE systems engineers, submit a [SIPE Request](https://apps.powerapps.com/play/e/9084dabc-d707-e1bf-8113-685be25cac15/a/cb338490-e23d-48e6-9e6d-e600b8d17dd4?tenantId=32669cd6-737f-4b39-8bdd-d6951120d3fc&hidenavbar=true) if you need watchdog installed somewhere.

Github actions are used to compile aind-watchdog-service into an executable using pyinstaller. This executable is attached to a Github Release and uploaded to SIPE's software /releases storage, where it can be deployed to rigs in the institute.

Watchdog should be deployed on a SIPE-accessible rig via the install-aind_watchdog_service.yml ansible script. This will download the app and set up a windows scheduled task to run the service. By default, the scheduled task includes a nightly restart at 11:30pm. If the task is running, the scheduler will kill the old instance and start the new one - due to how task scheduler force kills processes, the death of an old process will not generate a stop log.

## Testing
Watchdog has a bundled test function that will create some dummy data on the rig and create a manifest file to transfer that data. Once  Run it with the below commands:

```
cd "C://Program Files/AIBS_MPE/aind_watchdog_service"
aind_watchdog_service.exe --test
```

It will print something like the following:
```
Data created at C:\ProgramData\AIBS_MPE\aind_watchdog_service\logs\test_data
Manifest created at C:\Users\svc_mpe\Documents\aind_watchdog_service\manifest\test_manifest_2024-11-15_17-31-25.yml
```

Make sure that the running instance of watchdog picks up the manifest and completes the transfer.

## Development

Clone the repo
Install dependencies with `uv sync --extra service --extra dev`
Run the service with `uv run src/aind_watchdog_service/main.py`

(If not using uv, use `pip install .[service]` and `python src/aind_watchdog_service/main.py`)