There are several useful ways of engineering the context used by LLMs for different use cases.
MCP allows anybody to extend their own LLM application's context and capabilities using pre-built *third party* tools.
Agent Skills allows you to let the LLM enrich and narrow down it's own context based on the nature of the task it's doing.
I have been using a home grown version of Agent Skills for months now with Claude in VSCode, using skill files and extra tools in folders for the LLM to use. Once you have enough experience writing code with LLMs, you will realize this is a natural direction to take for engineering the context of LLMs. Very helpful in pruning unnecessary parts from "general instruction files" when working on specific tasks - all orchestrated by the LLM itself. And external tools for specific tasks (such as finding out which cell in a jupyter notebook contains the code that the LLM is trying to edit, for example) make LLMs a lot more accurate and efficient, efficient because they are not burning through precious tokens to do the same and accurate because the tools are not stochastic.
With Claude Skills now I don't need to maintain my home grown contraption. This is a welcome addition!
MCP allows anybody to extend their own LLM application's context and capabilities using pre-built *third party* tools.
Agent Skills allows you to let the LLM enrich and narrow down it's own context based on the nature of the task it's doing.
I have been using a home grown version of Agent Skills for months now with Claude in VSCode, using skill files and extra tools in folders for the LLM to use. Once you have enough experience writing code with LLMs, you will realize this is a natural direction to take for engineering the context of LLMs. Very helpful in pruning unnecessary parts from "general instruction files" when working on specific tasks - all orchestrated by the LLM itself. And external tools for specific tasks (such as finding out which cell in a jupyter notebook contains the code that the LLM is trying to edit, for example) make LLMs a lot more accurate and efficient, efficient because they are not burning through precious tokens to do the same and accurate because the tools are not stochastic.
With Claude Skills now I don't need to maintain my home grown contraption. This is a welcome addition!