# Interactive: pick an agent and scope
px setup mcp

# Register the Phoenix MCP server with a specific agent
px setup mcp --agent claude
px setup mcp --agent cursor --global --endpoint https://phoenix.example.com

# Headless, with an API-key header fallback for non-interactive environments
px setup mcp --agent codex --no-input --header 'Authorization: Bearer ${PHOENIX_API_KEY}'

from phoenix.evals.metrics.user_friction import UserFrictionEvaluator
from phoenix.evals import LLM

llm = LLM(provider="openai", model="gpt-4o-mini")
user_friction_eval = UserFrictionEvaluator(llm=llm)

scores = user_friction_eval.evaluate(
    {
        "conversation": (
            "User: Show orders from this week.\n"
            "Assistant: Here are last month's orders."
        ),
        "user_message": "No, I asked for this week.",
    }
)
print(scores)
# [Score(name='user_friction', score=1.0, label='friction', ...)]

import { createUserFrictionEvaluator } from "@arizeai/phoenix-evals/llm";
import { openai } from "@ai-sdk/openai";

const evaluator = createUserFrictionEvaluator({ model: openai("gpt-4o-mini") });

const result = await evaluator.evaluate({
  conversation: "User: Show recent orders.\nAssistant: Here are last month's orders.",
  userMessage: "No, I asked for this week.",
});
console.log(result.label); // "friction"