# Experiment 1 Cell B — Agent-as-Tool + MCP baseline.
#
# Same underlying tool functions as Cell A, reached via MCP JSON-RPC over
# stdio. (Cell B latency) - (Cell A latency) = raw MCP transport overhead.
# See docs/experiment1_capture_plan.md for the full design.
#
# This cell is also shared with Experiment 2 (AaT baseline), so
# CONTRIBUTING_EXPERIMENTS lists both families.
#
# STATUS (2026-04-24): runner wired via scripts/aat_runner.py (#104).
# MCP server hardening landed; the canonical Insomnia
# Llama-3.1-8B Cell B capture for #25 is a follow-up.

EXPERIMENT_NAME="aat_mcp_baseline"
EXPERIMENT_CELL="B"
EXPERIMENT_FAMILY="exp1_mcp_overhead"
SCENARIO_SET_NAME="smartgrid_multi_domain"
SCENARIOS_GLOB="data/scenarios/multi_*.json"

MODEL_ID="openai/Llama-3.1-8B-Instruct"

ORCHESTRATION="agent_as_tool"
MCP_MODE="baseline"
TRIALS=3
ENABLE_SMARTGRID_SERVERS=1
CONTRIBUTING_EXPERIMENTS="exp1_mcp_overhead,exp2_orchestration"
SCENARIO_DOMAIN_SCOPE="multi_domain"

MODEL_PROVIDER="vllm"
SERVING_STACK="insomnia_vllm"
QUANTIZATION_MODE="fp16"
# 32768 matches the repo's canonical benchmark-context lane. See #135.
MAX_MODEL_LEN=32768
TEMPERATURE=0.0
MAX_TOKENS=0

LAUNCH_VLLM=1
VLLM_MODEL_PATH="models/Llama-3.1-8B-Instruct"
VLLM_PORT=8000
VLLM_ENABLE_AUTO_TOOL_CHOICE=1
VLLM_TOOL_CALL_PARSER="llama3_json"

ENABLE_WANDB=1
WANDB_ENTITY="assetopsbench-smartgrid"
WANDB_PROJECT="assetopsbench-smartgrid"
WANDB_MODE="online"

# Agent-as-Tool dispatch now defaults to scripts/aat_runner.py. Set
# AAT_RUNNER_TEMPLATE only for custom parity/variant smoke commands.
AAT_MCP_SERVER_LAUNCH_MODE="python"
AAT_MCP_CLIENT_TIMEOUT_SECONDS=120
AAT_PARALLEL_TOOL_CALLS=false

# Torch profiler: captures one replay pass per run while vLLM is still live.
# Trace lands in profiling/traces/<RUN_ID>_torch/. Override: TORCH_PROFILE=0 sbatch ...
TORCH_PROFILE="${TORCH_PROFILE:-1}"