Context engineering > prompt engineering.
As MCP-based systems scale, the real challenge isn't giving models more tools — it's helping them retrieve and reason with the right ones at the right time.
Our Developer Advocate, Ashley Weaver explores RAG-MCP, a lightweight retrieval framework that uses semantic search to avoid context rot and keep tool selection accurate. Think: embed, rank, inject, reason.
The results? 3x better tool accuracy, 50%+ fewer tokens, and a cleaner path to production-grade agentic systems.
Full breakdown + working prototype 👇
MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
As MCPThe Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.Full definition → tool catalogs grow, loading every definition degrades selection accuracy. Retrieving a ranked subset per request is reported to roughly triple tool accuracy and halve tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → use, with a prototype to copy.
