With these patterns emerging, does anyone know how local LLMs are faring?
It seems to me that by combining MCP and "skills", we are adopting LLMs to be more useful tools; with MCP we restrict input and output when dealing with APIs so that the LLM can do what it is good at; translate between languages - in this case from English to various json subsets - and back.
And with skills we're serializing and formalizing prompts/context - narrowing the search space.
So that "summarize q1 numbers" gets reduced to "pick between these tools/MCP calls and parameterize on q1" - rather than the open ended task of "locate the data" and "try to match a sequence of tokens representing numbers - and generate tokens that look like a summary".
Given that - can we get away with much stupider LLMs for these types of use cases now - vs before we had these patterns?
It seems to me that by combining MCP and "skills", we are adopting LLMs to be more useful tools; with MCP we restrict input and output when dealing with APIs so that the LLM can do what it is good at; translate between languages - in this case from English to various json subsets - and back.
And with skills we're serializing and formalizing prompts/context - narrowing the search space.
So that "summarize q1 numbers" gets reduced to "pick between these tools/MCP calls and parameterize on q1" - rather than the open ended task of "locate the data" and "try to match a sequence of tokens representing numbers - and generate tokens that look like a summary".
Given that - can we get away with much stupider LLMs for these types of use cases now - vs before we had these patterns?