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This paper introduces SCOUT (Selective Context Optimization for Universal Tooling), a novel approach to optimizing the tool discovery and context management challenges faced by large language model (LLM) agents when interfacing with the Model Context Protocol (MCP). By employing a hybrid retrieval method that combines BM25 sparse matching with dense vector search, SCOUT effectively reduces the context window saturation from 140.2k tokens to just 1.3k tokens, leading to a 99% reduction in token consumption per query. Implemented in production at PayPal, SCOUT not only enhances tool discoverability among over 2,000 indexed tools but also maintains model-agnostic compatibility without requiring client-side changes.
SCOUT slashes tool-token consumption by 99%, revolutionizing how LLMs manage context and discover tools in enterprise environments.
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 providing a secure, governable chokepoint for authentication, policy enforcement, and observability. This architecture creates two compounding challenges: a context-engineering bottleneck where full tool schemas saturate the model context window before any user query, and a tool discoverability barrier where users and agents cannot identify the best tool among 2,000+ indexed tools across 200+ MCP servers. Prompt caching reduces reprocessing cost but neither frees context capacity nor improves accuracy. We present SCOUT (Selective Context Optimization for Universal Tooling), which reframes tool exposure as a context-selection problem, injecting only tools relevant to the current step. SCOUT surfaces two MCP meta-tools -- tool_search and execute_tool -- where tool_search performs hybrid retrieval, fusing BM25 sparse matching with dense vector search via Reciprocal Rank Fusion to return the top-k relevant tools. Backed by zero-downtime catalog update pipelines, SCOUT resolves both context saturation and tool discovery challenges. In production at PayPal, SCOUT reduces MCP tool-token consumption from 140.2k tokens (70.1% of context) to 1.3k tokens (0.8%), a 99% reduction, cutting per-query inference cost at enterprise scale. Because SCOUT is surfaced as standard MCP tools, it is model-agnostic and requires no client-side modifications.
