They plan to use to for "Code Mode" which mean the LLM will use this to run Python code that it writes to run tools instead of having to load the tools up front into the LLM context window.

(Pydantic AI lead here) We’re implementing Code Mode in https://github.com/pydantic/pydantic-ai/pull/4153 with support for Monty and abstractions to use other runtimes / sandboxes.

The idea is that in “traditional” LLM tool calling, the entire (MCP) tool result is sent back to the LLM, even if it just needs a few fields, or is going to pass the return value into another tool without needing to see the intermediate value. Every step that depends on results from an earlier step also requires a new LLM turn, limiting parallelism and adding a lot of overhead.

With code mode, the LLM can chain tool calls, pull out specific fields, and run entire algorithms using tools with only the necessary parts of the result (or errors) going back to the LLM.

These posts by Cloudflare: https://blog.cloudflare.com/code-mode/ and Anthropic: https://platform.claude.com/docs/en/agents-and-tools/tool-us... explain the concept and its advantages in more detail.