Making MCP Work Better in Small Models: Tool Calling Improvements and Context Methods for SLMs

MCP Dev Summit Toronto 2026

Oct 5, 2026 · 13:55–14:20 · Timezone not confirmed

While the Model Context Protocol (MCP) has seen strong enthusiasm and rapid iteration in the developer community, translating that success into broader industry adoption, particularly among small and medium enterprises, remains a major challenge. Many SMEs run AI agents locally to keep sensitive data on premises. These deployments typically use mid-sized open-weight models in the 8B to 32B parameter range. In practice, tool calling and MCP performance degrade sharply for these models under realistic contextual overload. In this session we present a systematic benchmarking study of tool calling and MCP behavior across multiple models. We evaluate performance on two benchmarks: τ²-bench and CEO-Bench. A key finding is that introducing standardized MCP OAuth and RBAC alone improves task completion accuracy by over 60%. We also demonstrate how combining MCP with skill and routing LLMs, together with targeted context engineering techniques such as context cleaning and context mutation, significantly boosts overall reliability, reduces tool-use errors in multi-turn scenarios, and markedly improves interpretability.

Session status: scheduled

Room: Giovanni

Track: Enterprise Adoption + Integration

Level: Intermediate

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In person · The Conference Centre at the University of Toronto, Toronto, Ontario, Canada

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