title: 'Order the Lab: a tool-calling trace, and what the model said with no tools'
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Order the Lab: a tool-calling trace, and what the model said with no tools
MIS 752 Lab 6, Week 6 (Function / Tool Calling). qwen2.5:3b-instruct on ollama was given 2 Python tools (forecast_ed_arrivals, get_drug_info) and asked a formulary question. data/ holds the trace stage by stage (trace_stages.csv), the deliverable as plain text (the_trace.txt), the four-question tools-on/tools-off grounding sweep, the per-turn token envelope, the tool registry, and this lab's registry rendered as an MCP tools/list response (mcp_tools_list.json, protocol 2026-07-28).
Every drug in this lab is invented. The formulary is a teaching stub; no dosing information appears anywhere and nothing here is clinical guidance.
Your ~150-word takeaways go here -- edit this README on huggingface.co. Cover: which tool-calling path your model actually used and how often the native one failed, what the tools-off answer did when it had no way to look anything up, what the prompt-based fallback cost you in tokens, and where this exact pattern shows up outside healthcare.