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SCOUT system slashes LLM tool-token usage by 99% at PayPal

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) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SCOUT system slashes LLM tool-token usage by 99% at PayPal

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The cluster describes a research paper detailing a new system for LLM agents.
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COVERAGE [2]

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    Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation

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  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Srinivasan Manoharan ·

    Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation

    Large language model (LLM) agents invoke external tools to retrieve and reason over information beyond pretrained knowledge. The Model Context Protocol (MCP) standardizes how such tools are surfaced, and a proxy MCP server aggregates many backend servers behind a single endpoint …