It's good idea. I didn't think of it because this project came about a "let's try to write a remote MCP server now that the standard has stabilized."
But there are some issues:
1. Cheaper + Deterministic: It is much more costly, both in terms of tokens and context window. (Generating the code takes many more tokens than making a tool call.) And there can be variability in the query, like issues with timezones.
2. Portability: It is not portable, not all LLM or LM environments have access to a code interpreter. This is a much lower resource requirement.
3. Extensibility: This approach is extensible, and it allows us to expand the toolkit with additional cognitive scaffolds that help contextualize how we experience time for the model. (This is a fancy way of saying: The code only gives the timestamp, but building an MCP allows us to contextualize this information — "this is time I'm sleeping, this is the time I'm eating or commuting, etc.")
4. Security: Ops teams are happier approving a read-only REST call than arbitrary code running.
But there are some issues:
1. Cheaper + Deterministic: It is much more costly, both in terms of tokens and context window. (Generating the code takes many more tokens than making a tool call.) And there can be variability in the query, like issues with timezones.
2. Portability: It is not portable, not all LLM or LM environments have access to a code interpreter. This is a much lower resource requirement.
3. Extensibility: This approach is extensible, and it allows us to expand the toolkit with additional cognitive scaffolds that help contextualize how we experience time for the model. (This is a fancy way of saying: The code only gives the timestamp, but building an MCP allows us to contextualize this information — "this is time I'm sleeping, this is the time I'm eating or commuting, etc.")
4. Security: Ops teams are happier approving a read-only REST call than arbitrary code running.