Choosing Tool-Use Benchmarks for Agent Models

Chat leaderboards answer "can it talk?" Tool-use benchmarks answer "can it operate?"

If your product is an agent, the second question is the one that burns production.

The decision dimensions

Dimension	Why it matters
Scenario count	More coverage vs longer runs
Determinism	Reproducible CI gates need mocked tools and fixed temperature
Safety coverage	Injection and restraint failures are expensive
Serving realism	OpenAI-compatible local stacks behave differently than vendor APIs
Trace quality	You need to debug fails, not just collect a number

Two practical options

ToolCall-15

• 15 scenarios, 5 categories
• Excellent as a fast regression pack
• BenchLocal-friendly for desktop comparison

tool-eval-bench

• 69 core scenarios + optional Hard Mode
• Broader categories including safety, scale, structured output
• Built for OpenAI-compatible serving stacks (vLLM, llama.cpp, etc.)

Use ToolCall-15 on every model bump. Run tool-eval-bench when choosing a new default model or after tool-schema changes.

How to interpret scores

• Do not average across different suite versions
• Track safety category separately from overall score
• Prefer trend lines on your stack over absolute online leaderboard claims
• A model that scores high but fails restraint tests is not production-ready for unsupervised tools

Recommended policy

1. Gate merges on a small deterministic suite
2. Weekly or per-release run a larger suite
3. Keep raw traces for the bottom 10 failing scenarios
4. Fix harness issues before swapping models

Bottom line

Tool-use benchmarks are harnesses for model choice. Pick the suite that matches your failure modes, then run it on a schedule.