Tool Evaluation

Wrong tool calls cause real side effects — deleted records, wrong emails. Evaluating tool behavior isolates the riskiest failure mode in agentic systems.

Tool eval is checking a surgeon picks the right instrument — a brilliant diagnosis means nothing if they grab a scalpel when they needed a suture.

Scenario

Query 'What's my balance?' should call `get_balance` not `transfer_funds` — eval flags 3/100 misfires on ambiguous phrasing.

Solution

In Agent Evaluation & Observability, apply Tool Evaluation to this scenario: Query 'What's my balance?' should call `get_balance` not `transfer_funds` — eval flags 3/100 misfires on ambiguous phrasing. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Tool Evaluation (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Tool Evaluation happens in the code.

1def tool_selection_score(trace, expected_tool):  # define a reusable function2    actual = next(s for s in trace if s.type == "tool_call")  # key line for Tool Evaluation3    return actual.name == expected_tool  # return the result4
5assert tool_selection_score(run("balance?"), "get_balance")  # key line for Tool Evaluation

• Treating Tool Evaluation as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for tool evaluation

Quick recap — the most important points from this module.