MCP doesn't help but filtering is not always a good solution - sometimes you just need the agent to process a lot of data.
In that scenario running code on the data with minimum evaluation of the data (eg. a schema with explanation) is a much better approach and it will scale up to use cases of a certain complexity.
Even this system is not perfect: once your data definition and orchestration grow to big you'll face the same problems.
This should allow you to scale to pretty complex problems though, while the naive approach of just embedding API responses in the chat fails soon (I run into this issue frequently, maintaining a relatively simple systems with a few tool calls).
The only proper solution is reproducing the level of granularity of human decisions in code and call this "decisional system" from an LLM (which would be then reduced to a mere language interface between human language and the internal system).
Easier said than done, though.
Comments
MCP doesn't help but filtering is not always a good solution - sometimes you just need the agent to process a lot of data.
In that scenario running code on the data with minimum evaluation of the data (eg. a schema with explanation) is a much better approach and it will scale up to use cases of a certain complexity.
Even this system is not perfect: once your data definition and orchestration grow to big you'll face the same problems.
This should allow you to scale to pretty complex problems though, while the naive approach of just embedding API responses in the chat fails soon (I run into this issue frequently, maintaining a relatively simple systems with a few tool calls).
The only proper solution is reproducing the level of granularity of human decisions in code and call this "decisional system" from an LLM (which would be then reduced to a mere language interface between human language and the internal system). Easier said than done, though.