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Multi-Turn MCP-Use

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

The Multi-Turn MCP Use metric is a conversational metric that uses LLM-as-a-judge to evaluate how effectively an MCP based LLM agent makes use of the mcp servers it has access to. It evaluates the MCP primitives called as well as the arguments generated by the LLM app.

Required Arguments​

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

  • turns
  • mcp_servers

You will also need to provide mcp_tools_called, mcp_resources_called and mcp_prompts_called inside the turns whenever there is an MCP interaction in your agent's workflow. You can learn more about creating MCP test cases here.

Usage​

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

from deepeval import evaluate
from deepeval.metrics import MultiTurnMCPUseMetric
from deepeval.test_case import Turn, ConversationalTestCase, MCPServer

convo_test_case = ConversationalTestCase(
    turns=[Turn(role="...", content="..."), Turn(role="...", content="...")],
    mcp_servers=[MCPServer(...)]
)
metric = MultiTurnMCPUseMetric(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 are SIX optional parameters when creating a MultiTurnMCPUseMetric:

  • [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 MultiTurnMCPUseMetric 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, Confident AI platform) and all the optimizations (speed, caching, computation) the evaluate() function or deepeval test run offers.

How Is It Calculated​

The MultiTurnMCPUseMetric score is calculated according to the following equation:

MCP Use Score=AlignmentScore(Primitives Used, Primitives Available)Total Number of MCP Interactions\text{MCP Use Score} = \frac{\text{AlignmentScore(Primitives Used, Primitives Available)}}{\text{Total Number of MCP Interactions}}
  • The AlignmentScore is judged by an evaluation model based on which primitives were called and their generated arguments with respect to the task.
  • MCP Interactions are the number of times the LLM app uses the MCP server's capabilities.