Researchers have developed SCOUT (Selective Context Optimization for Universal Tooling), a system designed to address context-engineering and tool-discovery challenges for large language model (LLM) agents. SCOUT reframes tool exposure as a context-selection problem, injecting only relevant tools for the current step. This approach significantly reduces token consumption and inference costs, as demonstrated in production at PayPal where it achieved a 99% reduction in tool-token usage. SCOUT is model-agnostic and requires no client-side modifications, functioning as standard MCP tools. AI
IMPACT Reduces LLM inference costs and improves efficiency in enterprise applications by optimizing tool selection.
RANK_REASON The cluster describes a research paper detailing a new system for LLM agents.
Read on arXiv cs.IR (Information Retrieval) →
- BM25
- Large language model (LLM) agents
- Model Context Protocol (MCP)
- PayPal
- Reciprocal Rank Fusion
- SCOUT
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