arcade-mcp/libs/arcade-evals

Arcade Evals

Evaluation toolkit for testing Arcade tools.

Overview

Arcade Evals provides comprehensive evaluation capabilities for Arcade tools:

• Evaluation Framework: Cases, suites, and rubrics for systematic testing
• Critics: Different types of comparisons (binary, numeric, similarity, datetime)
• Tool Evaluation: Decorators and utilities for evaluating tool performance
• Result Analysis: Comprehensive evaluation results and reporting

Installation

pip install 'arcade-ai[evals]'

Usage

Basic Evaluation

from arcade_evals import EvalCase, EvalSuite, tool_eval

# Create evaluation cases
case1 = EvalCase(
    input={"query": "What is 2+2?"},
    expected_output="4"
)

case2 = EvalCase(
    input={"query": "What is the capital of France?"},
    expected_output="Paris"
)

# Create evaluation suite
suite = EvalSuite(cases=[case1, case2])

# Evaluate a tool
@tool_eval(suite)
def my_calculator(query: str) -> str:
    # Tool implementation
    return "4" if "2+2" in query else "Unknown"

Using Critics

from arcade_evals import NumericCritic, SimilarityCritic

# Numeric comparison
numeric_critic = NumericCritic(tolerance=0.1)
result = numeric_critic.evaluate(expected=10.0, actual=10.05)

# Similarity comparison
similarity_critic = SimilarityCritic(threshold=0.8)
result = similarity_critic.evaluate(
    expected="The capital of France is Paris",
    actual="Paris is the capital of France"
)

Advanced Evaluation

from arcade_evals import EvalRubric, ExpectedToolCall

# Create rubric with tool calls
rubric = EvalRubric(
    expected_tool_calls=[
        ExpectedToolCall(
            tool_name="calculator",
            parameters={"operation": "add", "a": 2, "b": 2}
        )
    ]
)

# Evaluate with rubric
suite = EvalSuite(cases=[case1], rubric=rubric)

License

MIT License - see LICENSE file for details.