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Tool Use

LLM-as-a-judge
Multi-turn
Referenceless
Agent

The Tool Use metric is a multi-turn agentic metric that evaluates whether your LLM agent's tool selection and argument generation capablilities. It is a self-explaining eval, which means it outputs a reason for its metric score.

Required Arguments​

To use the ToolUseMetric, you'll have to provide the following arguments when creating a ConversationalTestCase:

  • turns

You can learn more about how it is calculated here.

Usage​

The ToolUseMetric() can be used for end-to-end multi-turn evaluations of agents.

from deepeval import evaluate
from deepeval.metrics import ToolUseMetric
from deepeval.test_case import Turn, ConversationalTestCase, ToolCall

convo_test_case = ConversationalTestCase(
    turns=[
        Turn(role="...", content="..."), 
        Turn(role="...", content="...", tools_called=[...])
    ],
)
metric = ToolUseMetric(threshold=0.5)

# To run metric as a standalone
# metric.measure(convo_test_case)
# print(metric.score, metric.reason)

evaluate(test_cases=[convo_test_case], metrics=[metric])

There is ONE mandatory and SIX optional parameters when creating a ToolUseMetric:

  • available_tools: a list of ToolCalls that give context on all the tools that were available to your LLM agent. This list is used to evaluate your agent's tool selection capability.
  • [Optional] threshold: a float representing the minimum passing threshold, defaulted to 0.5.
  • [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4o'.
  • [Optional] include_reason: a boolean which when set to True, will include a reason for its evaluation score. Defaulted to True.
  • [Optional] strict_mode: a boolean which when set to True, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to False.
  • [Optional] async_mode: a boolean which when set to True, enables concurrent execution within the measure() method. Defaulted to True.
  • [Optional] verbose_mode: a boolean which when set to True, prints the intermediate steps used to calculate said metric to the console, as outlined in the How Is It Calculated section. Defaulted to False.

As a standalone​

You can also run the ToolUseMetric on a single test case as a standalone, one-off execution.

...

metric.measure(convo_test_case)
print(metric.score, metric.reason)
caution

This is great for debugging or if you wish to build your own evaluation pipeline, but you will NOT get the benefits (testing reports) and all the optimizations (speed, caching, computation) the evaluate() function or deepeval test run offers.

How Is It Calculated​

The ToolUseMetric score is determined through the following process:

  1. Compute the Tool Selection Score for each unit interaction.
  2. Compute the Argument Correctness Score for all unit interactions that include tool calls.
Tool Use Score=min⁡(ToolSelectionScore,ArgumentCorrectnessScore)\text{Tool Use Score} = \min(\text{ToolSelectionScore}, \text{ArgumentCorrectnessScore})
  • The Tool Selection Score evaluates whether the agent chose the most appropriate tool for the task among all the available tools.
  • The Argument Correctness Score assesses whether the arguments provided in the tool call were accurate and suitable for the task. This score is only considered when a tool call has been made.