flyrain commented on issue #50: URL: https://github.com/apache/polaris-tools/issues/50#issuecomment-3628522128
On the “better context” side, I’m thinking about at least three options we could explore: ## MCP prompt / hints This is probably the easiest to start with: we keep enriching the MCP prompt/hints so the client sees more Polaris-specific guidance. The downside is that it requires updating the MCP server every time something changes, and we’re constrained by hint size. I also assume the hint is loaded in one shot on each invocation, which can burn a lot of tokens if it gets too large. Another downside is that URLs in hint are not allowed in MCP clients like Claude desktop. ## Claude Skill Here we put the Polaris context into a Claude Skill, which effectively behaves like a dynamic system prompt. It allows for much larger and more instructional context than a simple MCP hint and can be updated independently of the server. The limitation is that this only helps when the client is Claude, so it doesn’t generalize to other MCP clients. ## RAG over Polaris knowledge A more generic, “agent-style” approach: we chunk Polaris knowledge (docs, examples, maybe even relevant code snippets) into a vector store and let the LLM retrieve what it needs on demand. This avoids pushing all context up front and should scale better as the knowledge grows, at the cost of having to maintain the RAG pipeline and index. Curious what you think about trying a lightweight version of (1) first, and then layering (2)/(3) as we see how far we can get. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
