data/processed/RQ1/EmailResponder-MCP/reference_trajectory.yaml

# Complete Reference Trajectory Configuration - EmailResponder-MCP Project
#
# Version: Ideal trajectory based on code design (applicable to all models)
# Principle: Only include steps explicitly required by code design, not based on test statistics
#
# MCP Version Features:
# - Tools provided via MCP Server, tool names in lowercase+underscore format
# - Both Agents use mcp_adapter.tools (same tool set)
#
# Code Design Analysis (based on Mock mode):
# 1. Required tool: fetch_new_emails
#    - tasks.yaml explicitly requires this tool to fetch emails
#
# 2. Required tool: create_email_draft
#    - tasks.yaml: "If auto_create_drafts is enabled, use..."
#    - main.py: auto_create_drafts=True (hardcoded)
#
# 3. Optional tool: get_gmail_thread
#    - Not providing additional value in Mock mode (fetch_new_emails already returns complete info)

# Project name
project_name: "EmailResponder-MCP"

# Trajectory extraction type configuration
# Focus on: SPAN, Chain, AGENT, LLM, Tool
# Ignore: Task Created, Crew Created and other non-core content
extract_types:
  - "SPAN" # Level 1: Top-level execution span
  - "Chain" # Level 2: Crew execution chain
  - "AGENT" # Level 3: Agent execution
  - "LLM" # Level 4: LLM calls
  - "Tool" # Level 4: Tool calls

# ============================================================================
# Reference Trajectory (Ground Truth) - Based on Code Design (MCP Version)
# ============================================================================
#
# Graphical Structure (ideal execution path):
#
# [SPAN] email_auto_responder
# └─ [Chain] Crew***.kickoff
#    ├─ [AGENT] Senior Email Analysis and Classification Specialist  ← Task 1
#    │  ├─ [LLM] <model>  ← Decide to call tool
#    │  ├─ [Tool] fetch_new_emails  ← Required: fetch emails (MCP tool name format)
#    │  └─ [LLM] <model>  ← Process tool result
#    │
#    ├─ [AGENT] Senior Email Analysis and Classification Specialist  ← Task 2
#    │  └─ [LLM] <model>  ← Analyze emails (no tool call)
#    │
#    └─ [AGENT] Expert Professional Email Response Composer  ← Task 3
#       ├─ [LLM] <model>  ← Generate reply content
#       ├─ [Tool] create_email_draft  ← Required: create draft (MCP tool name format)
#       └─ [LLM] <model>  ← Confirm completion
#
# Design Rationale (based on Mock mode):
# - Task 1: tasks.yaml explicitly requires fetch_new_emails
# - Task 2: In Mock mode, fetch_new_emails already returns all info (snippet),
#           get_gmail_thread won't provide additional value, so not called
# - Task 3: tasks.yaml requires creating draft when auto_create_drafts=True
#           main.py: auto_create_drafts=True (hardcoded)
#
# Notes:
# - <model> represents any LLM, not limited to specific model
# - MCP tool names use lowercase+underscore format (fetch_new_emails, create_email_draft)
# ============================================================================

reference_trajectory:
  # ===== Level 1: SPAN =====
  - "SPAN: email_auto_responder"

  # ===== Level 2: Chain (wildcard for UUID) =====
  - "Chain: Crew***.kickoff"

  # ===== Task 1: fetch_emails_task =====
  # Required: fetch_new_emails (MCP tool, lowercase+underscore format)
  - "Agent: Senior Email Analysis and Classification Specialist"
  - "LLM: *" # Any model
  - "Tool: fetch_new_emails"
  - "LLM: *"

  # ===== Task 2: filter_emails_task =====
  # Only analyze emails, no tool call (in Mock mode fetch_new_emails already returns all info)
  - "Agent: Senior Email Analysis and Classification Specialist"
  - "LLM: *"

  # ===== Task 3: generate_responses_task =====
  # Required: create_email_draft (MCP tool, auto_create_drafts=True)
  - "Agent: Expert Professional Email Response Composer and Communication Strategist"
  - "LLM: *"
  - "Tool: create_email_draft"
  - "LLM: *"

# Target tools list (for single-tool use metric)
# Only includes actual tool calls, used to detect tool usage
target_tools:
  # MCP tools (lowercase+underscore format)
  - "Tool: fetch_new_emails"
  - "Tool: get_gmail_thread"
  - "Tool: create_email_draft"

# Models to evaluate
models:
  - "GPT-5"
  - "GPT-4o-mini"
  - "DeepSeek-V3-1"
  - "DeepSeek-R1"
  - "Gemini-2.5-flash"
  - "Gemini-2.5-flash-nothinking"
  - "Qwen3-235b"
# Usage Instructions:
#
# 1. Trajectory Hierarchy:
#    - Top: SPAN (email_auto_responder)
#    - Level 2: Chain (Crew***.kickoff, wildcard for UUID)
#    - Level 3: AGENT (specific Agent name, must match exactly)
#    - Level 4: LLM and Tool (alternating)
#
# 2. Wildcard Handling:
#    - Crew_b02af339-6c40-4ecb-b174-ec6fbe8e2081.kickoff -> Crew***.kickoff
#    - Script automatically converts Crew_UUID.kickoff format to Crew***.kickoff
#
# 3. Exact Match Requirements:
#    - SPAN name: exact match "email_auto_responder"
#    - Chain name: wildcard match "Crew***.kickoff"
#    - AGENT name: exact match (e.g., "Senior Email Analysis and Classification Specialist")
#    - LLM name: exact match model name (e.g., "gpt-4o-mini")
#    - Tool name: exact match tool name (e.g., "fetch_new_emails")
#
# 4. Evaluation Metrics:
#    - Exact Match: requires all nodes to be identical (including LLM call count)
#    - In-order Match: allows extra LLM calls, but core sequence must be in order
#    - Any-order Match: only requires all necessary nodes to be present
#    - Precision/Recall: calculates accuracy and recall for all nodes
#
# 5. Run Command:
#    python3 evaluate_trajectory.py --config reference_trajectory.yaml
#
# 6. Notes:
#    - Different models may have different LLM call counts (thinking, retries, etc.)
#    - Complete trajectory evaluation is stricter than Tool-only evaluation
#    - Exact Match scores may be low, focus on In-order Match and Any-order Match