Code Execution with MCP: Architecting Agentic Efficiency

The scalability of multi-tool agent systems built on the Model Context Protocol (MCP) is severely limited by context window constraints. This limitation manifests as dual challenges: excessive token consumption from tool definitions (Tool Definition Bloat) and inefficient data passing via the context window (Tool Result Bloat). This article explores a powerful architectural shift, leveraging Large Language Models (LLMs) to dynamically generate and execute typed code stubs that wrap MCP servers as an agentic Software Development Kit (SDK). This approach fundamentally resolves context bloat by employing progressive disclosure and transferring intermediate data handling to a sandboxed execution environment, significantly enhancing control flow, speed, and cost-efficiency for production-scale deployments.