问HN：这里有人为他们的MCP服务器提供代码执行环境吗？
我建立了一个多通道处理器（MCP）来处理我所有的可穿戴设备数据，这非常有帮助，但这个代理能够回答的问题类型在没有访问权限来编写自己程序的情况下是有限的。单个可穿戴设备的数据流在1小时的活动中可以有20,000个数据点（以1Hz的频率记录GPS、气压计、温度和心率），而典型的MCP设计要么要求MCP作者手动定义聚合方法（例如，获取平均心率），要么大型语言模型（LLM）必须在其上下文中保持大量数据表示。
最近，我为MCP工具提供了创建和访问iPython内核的功能，在这个内核中，它可以在会话创建之前指定要下载的包，并且可以通过编写自己的代码来操作数据的副本。
确保数据保持私密，工具和代码的安全性是花费最多时间的部分，我在想是否有其他工具可以帮助简化这个过程。我知道有像e2b.dev这样的工具提供代码沙箱，但我觉得其他数据提供的MCP也会遇到这个问题，所以一定还有我遗漏的某种解决方案或架构设计。
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I built an MCP to handle all my wearables data, and it was super helpful, but the types of questions that agent could answer without access to write its own programs was limited. A single wearables stream can have 20k data points for 1 hr of activity (1Hz across GPS, barometer, temperature, and HR), and the typical MCP design either has the MCP author manually defining the aggregation methods (e.g., get_average_heartrate) or the LLM has to hold in it's context the large data representation.<p>I recently gave the MCP tools to create and access an iPython kernel, where it has the ability to specify what package to download before the session is created, and it can manipulate a copy of the data by writing its own code.<p>Making sure data was kept private and tools / code were secure was what took most of the time, and I'm wondering if there are any tools folks are using to make this easier. I know there are tools like e2b.dev which provide code sandboxes, but I feel like other data providing MCPs will run into this issue, so there must be some solution / architecture design I'm missing.