How to expose tools to AI agents without clogging their context

AI agents can handle large catalogs of connected tools more effectively by exposing a small set of frequently needed tools, making other permitted capabilities searchable on demand, and limiting both tool-definition and response sizes to preserve context. Testing with 57 tools found that on-demand discovery with two pinned tools reduced cumulative input tokens by 69% compared with loading all definitions, while still completing meeting-preparation tasks. The approach distinguishes user authorization, session-level permissions, and model-visible tools, with execution-layer controls required to prevent agents from expanding their access through guessed calls or retrieved instructions. Developers can improve reliability through task-specific tools, application-state-based routing, typed schema validation across listing and discovery paths, compact and paginated outputs, and backend processing for repetitive or large-scale operations. MCP alone does not ensure selective tool loading, so applications must actively control visibility, permissions, schemas, and response design while evaluating task completion, incorrect tool use, discovery failures, token consumption, and latency.

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