code/scripts/orchestration_utils.py

from __future__ import annotations

import argparse
import importlib
import json
import logging
import os
import re
import sys
from contextlib import AsyncExitStack
from dataclasses import dataclass
from datetime import timedelta
from pathlib import Path
from types import ModuleType, SimpleNamespace
from typing import Any

LOG_FORMAT = "%(asctime)s  %(levelname)-8s  %(name)s  %(message)s"
LOG_DATE_FORMAT = "%H:%M:%S"
MAX_CONTEXT_RESPONSE_CHARS = 1200
MAX_CONTEXT_ERROR_CHARS = 600
_LOG = logging.getLogger(__name__)

SUMMARIZE_PROMPT = """\
You are summarizing the results of a multi-step task execution for an
industrial asset operations system.

Original question: {question}

If there was a self-ask clarification pass, use it only as internal guidance
and still answer the original question directly.

Step-by-step execution results:
{results}

Provide a concise, direct answer to the original question based on the results
above. Do not repeat the individual steps - just give the final answer.
"""

SUMMARIZE_ADJUDICATION_BLOCK = """\

Explicit fault/risk adjudication:
{adjudication}

If the adjudication decision is "finalize", cite the deciding evidence in the
final answer and do not introduce a different fault or risk choice. This block
is only a constraint on fault/risk claims, not a replacement for the original
task. Still answer every requested part of the original question using the
other execution results. If the decision is "refuse_due_missing_evidence",
refuse to finalize only the unsupported maintenance recommendation and state
what evidence is missing.
"""

SELF_ASK_PROMPT = """\
You are deciding whether an industrial asset-operations question needs a brief
internal clarification pass before tool planning.

Return a single raw JSON object with exactly these keys:
- needs_self_ask: boolean
- clarifying_questions: list of at most 2 short strings
- assumptions: list of at most 3 short strings
- augmented_question: string

Rules:
- Use needs_self_ask=false when the original question is already specific enough.
- If needs_self_ask=false, set augmented_question to the original question.
- If needs_self_ask=true, augmented_question should keep the original question
  intact while appending the clarification points the planner should resolve
  internally before answering.
- Do not ask the human user for clarification. This is an internal planning aid.

Question:
{question}

JSON:
"""

VERIFIER_PROMPT = """\
You are verifying whether a completed plan-execute step actually advanced the
goal enough to continue without repair.

Return a single raw JSON object with exactly these keys:
- decision: one of "continue", "retry", "replan_suffix"
- reason: short string
- updated_focus: short string, or empty string if not needed

Rules:
- Prefer "continue" unless the current result clearly suggests the remaining
  plan should change.
- Use "retry" only when the same step should be attempted once more with the
  same overall intent.
- Use "replan_suffix" when the completed context changes what the remaining
  steps should be.
- Keep the answer benchmarkable: avoid open-ended or conversational behavior.

Original question:
{question}

Effective planning question:
{effective_question}

Current step:
{current_step}

Current step result:
{current_result}

Completed history so far:
{history}

Remaining planned steps:
{remaining_steps}

JSON:
"""


@dataclass
class SelfAskDecision:
    needs_self_ask: bool
    clarifying_questions: list[str]
    assumptions: list[str]
    augmented_question: str


@dataclass
class VerificationDecision:
    decision: str
    reason: str
    updated_focus: str


@dataclass
class MissingEvidenceRepairConfig:
    enabled: bool
    max_attempts: int
    max_attempts_per_target: int


@dataclass
class FaultRiskAdjudicationConfig:
    enabled: bool


def load_fault_risk_adjudication_config() -> FaultRiskAdjudicationConfig:
    """Read default-off explicit fault/risk adjudication config."""
    from mitigation_guards import env_flag_enabled

    enabled = env_flag_enabled(
        os.environ.get("ENABLE_EXPLICIT_FAULT_RISK_ADJUDICATION")
    )
    guard_enabled = env_flag_enabled(os.environ.get("ENABLE_MISSING_EVIDENCE_GUARD"))
    if enabled and not guard_enabled:
        raise RuntimeError(
            "ENABLE_EXPLICIT_FAULT_RISK_ADJUDICATION=1 requires "
            "ENABLE_MISSING_EVIDENCE_GUARD=1 so adjudicated runs keep the "
            "truthfulness/accounting gate active."
        )
    return FaultRiskAdjudicationConfig(enabled=enabled)


def load_missing_evidence_repair_config() -> MissingEvidenceRepairConfig:
    """Read default-off missing-evidence recovery config from environment."""
    from mitigation_guards import env_flag_enabled

    enabled = env_flag_enabled(os.environ.get("ENABLE_MISSING_EVIDENCE_REPAIR"))
    guard_enabled = env_flag_enabled(os.environ.get("ENABLE_MISSING_EVIDENCE_GUARD"))
    if enabled and not guard_enabled:
        raise RuntimeError(
            "ENABLE_MISSING_EVIDENCE_REPAIR=1 requires "
            "ENABLE_MISSING_EVIDENCE_GUARD=1 so repaired runs keep the "
            "truthfulness/accounting gate active."
        )
    if not enabled:
        return MissingEvidenceRepairConfig(
            enabled=False,
            max_attempts=0,
            max_attempts_per_target=0,
        )
    return MissingEvidenceRepairConfig(
        enabled=enabled,
        max_attempts=_positive_int_env("MISSING_EVIDENCE_REPAIR_MAX_ATTEMPTS", 2),
        max_attempts_per_target=_positive_int_env(
            "MISSING_EVIDENCE_REPAIR_MAX_ATTEMPTS_PER_TARGET",
            1,
        ),
    )


def _positive_int_env(name: str, default: int) -> int:
    raw = os.environ.get(name, str(default))
    try:
        value = int(raw)
    except ValueError as exc:
        raise RuntimeError(f"{name} must be a positive integer, got {raw!r}.") from exc
    if value < 1:
        raise RuntimeError(f"{name} must be a positive integer, got {raw!r}.")
    return value


def build_missing_evidence_repair_state(
    config: MissingEvidenceRepairConfig,
) -> dict[str, Any]:
    from mitigation_guards import (
        MISSING_EVIDENCE_GUARD_NAME,
        MISSING_EVIDENCE_REPAIR_NAME,
    )

    return {
        "name": MISSING_EVIDENCE_REPAIR_NAME,
        "enabled": bool(config.enabled),
        "detector": MISSING_EVIDENCE_GUARD_NAME,
        "triggered": False,
        "attempts": [],
        "repaired": False,
        "final_decision": "disabled" if not config.enabled else "not_triggered",
    }


def current_missing_evidence_hit(
    history: list[dict[str, Any]],
    current_entry: dict[str, Any],
) -> dict[str, Any] | None:
    """Return the unresolved detector hit caused by the current history entry."""
    if not history:
        return None
    from mitigation_guards import scan_missing_evidence

    scan = scan_missing_evidence({"history": history})
    if not scan["triggered"]:
        return None

    current_source_prefix = f"history[{len(history) - 1}]"
    current_step = current_entry.get("step")
    for hit in scan["hits"]:
        source = str(hit.get("source") or "")
        if source == current_source_prefix or source.startswith(
            current_source_prefix + "."
        ):
            return hit
    for hit in scan["hits"]:
        if current_step is not None and hit.get("step") == current_step:
            return hit
    return None


def repair_target_key(hit: dict[str, Any]) -> tuple[str, tuple[tuple[str, str], ...]]:
    target = hit.get("target")
    target_parts = ()
    if isinstance(target, dict):
        target_parts = tuple(sorted((str(k), str(v)) for k, v in target.items()))
    return (str(hit.get("tool") or ""), target_parts)


def can_retry_missing_evidence(
    config: MissingEvidenceRepairConfig,
    state: dict[str, Any],
    hit: dict[str, Any],
    target_attempts: dict[tuple[str, tuple[tuple[str, str], ...]], int],
) -> bool:
    if not config.enabled:
        return False
    if len(state.get("attempts", [])) >= config.max_attempts:
        return False
    key = repair_target_key(hit)
    return target_attempts.get(key, 0) < config.max_attempts_per_target


def record_missing_evidence_retry_attempt(
    state: dict[str, Any],
    hit: dict[str, Any],
    *,
    action: str = "retry_step",
) -> dict[str, Any]:
    state["triggered"] = True
    state["final_decision"] = "repair_attempted"
    attempt = {
        "attempt_index": len(state.get("attempts", [])) + 1,
        "source_step": hit.get("step"),
        "tool": hit.get("tool"),
        "target": hit.get("target") or {},
        "reason": hit.get("reason"),
        "action": action,
        "result": "scheduled",
    }
    state.setdefault("attempts", []).append(attempt)
    return attempt


def mark_missing_evidence_attempt_result(
    state: dict[str, Any],
    attempt: dict[str, Any] | None,
    result: str,
    *,
    new_step: int | None = None,
) -> None:
    if attempt is None:
        return
    attempt["result"] = result
    if new_step is not None:
        attempt["new_step"] = new_step
    if result == "repaired":
        state["repaired"] = True
        state["final_decision"] = "continue"


def mark_missing_evidence_unrepaired(
    state: dict[str, Any],
    hit: dict[str, Any] | None,
) -> None:
    if hit:
        state["triggered"] = True
        state["final_decision"] = "block_finalization"
    elif state.get("triggered") and state.get("final_decision") == "repair_attempted":
        state["final_decision"] = "continue"


def finalize_missing_evidence_repair_state(
    state: dict[str, Any],
    history: list[dict[str, Any]],
) -> None:
    """Finalize repair metadata against the terminal history."""
    if not state.get("enabled"):
        return
    from mitigation_guards import scan_missing_evidence

    scan = scan_missing_evidence({"history": history})
    if scan["triggered"]:
        state["triggered"] = True
        state["final_decision"] = "block_finalization"
        state["unresolved_hits"] = scan["hits"]
        return
    if state.get("triggered"):
        state["repaired"] = True
        state["final_decision"] = "continue"
    else:
        state["final_decision"] = "not_triggered"


def build_fault_risk_adjudication_state(
    question: str,
    history: list[dict[str, Any]],
    config: FaultRiskAdjudicationConfig,
) -> dict[str, Any]:
    from mitigation_guards import build_explicit_fault_risk_adjudication

    return build_explicit_fault_risk_adjudication(
        {"question": question, "history": history},
        enabled=config.enabled,
    )


def fault_risk_adjudication_failed_step(
    adjudication: dict[str, Any],
) -> dict[str, Any] | None:
    if adjudication.get("decision") != "refuse_due_missing_evidence":
        return None
    from mitigation_guards import adjudication_failed_step

    return adjudication_failed_step(adjudication)


def setup_logging(verbose: bool) -> None:
    level = logging.INFO if verbose else logging.WARNING
    handler = logging.StreamHandler(sys.stderr)
    handler.setFormatter(logging.Formatter(LOG_FORMAT, datefmt=LOG_DATE_FORMAT))
    logging.root.handlers.clear()
    logging.root.addHandler(handler)
    logging.root.setLevel(level)


def build_parser(prog: str, description: str) -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(prog=prog, description=description)
    parser.add_argument("question", help="The question to answer.")
    parser.add_argument(
        "--model-id",
        default="watsonx/meta-llama/llama-4-maverick-17b-128e-instruct-fp8",
        metavar="MODEL_ID",
        help="litellm model string with provider prefix.",
    )
    parser.add_argument(
        "--server",
        action="append",
        metavar="NAME=PATH",
        dest="servers",
        default=[],
        help="Register an MCP server as NAME=PATH. Repeatable.",
    )
    parser.add_argument(
        "--mcp-mode",
        choices=("baseline", "optimized"),
        default=os.environ.get("MCP_MODE", "baseline"),
        help=(
            "MCP execution mode for repo-local PE-family runners. "
            "baseline opens stdio sessions per AOB Executor call; optimized "
            "reuses initialized MCP sessions across descriptions and tool calls."
        ),
    )
    parser.add_argument(
        "--aob-path",
        default="",
        metavar="PATH",
        help="Path to the sibling AssetOpsBench checkout.",
    )
    parser.add_argument(
        "--show-plan",
        action="store_true",
        help="Print the generated plan before execution.",
    )
    parser.add_argument(
        "--show-trajectory",
        action="store_true",
        help="Print each step result after execution.",
    )
    parser.add_argument(
        "--json",
        action="store_true",
        dest="output_json",
        help="Output the full result as JSON.",
    )
    parser.add_argument(
        "--verbose",
        action="store_true",
        help="Show INFO-level progress logs on stderr.",
    )
    return parser


def resolve_repo_root() -> Path:
    return Path(__file__).resolve().parents[1]


def resolve_aob_path(repo_root: Path, raw_path: str = "") -> Path:
    if raw_path:
        return Path(raw_path).resolve()
    return (repo_root.parent / "AssetOpsBench").resolve()


def bootstrap_aob(aob_path: Path) -> None:
    src_path = aob_path / "src"
    agent_src_path = src_path / "agent"
    if not src_path.exists():
        raise FileNotFoundError(f"AssetOpsBench src path not found: {src_path}")
    if not agent_src_path.exists():
        raise FileNotFoundError(f"AssetOpsBench agent path not found: {agent_src_path}")
    # AOB's PE family is imported both as package-level modules under src/ and
    # as nested agent modules under src/agent/. Keep both on sys.path so the
    # repo-local runners can import the plan_execute slice directly without
    # triggering unrelated package-level imports.
    for path in (src_path, agent_src_path):
        path_text = str(path)
        if path_text not in sys.path:
            sys.path.insert(0, path_text)
    if "agent" not in sys.modules:
        agent_package = ModuleType("agent")
        agent_package.__path__ = [str(agent_src_path)]  # type: ignore[attr-defined]
        agent_package.__package__ = "agent"
        sys.modules["agent"] = agent_package


def preflight_aob_runtime_dependencies() -> None:
    failures = []
    checks = {
        "litellm": "AssetOpsBench LiteLLM backend",
        "mcp": "AssetOpsBench plan-execute MCP client helpers",
    }
    install_hint = (
        "Install the shared repo dependencies with `uv pip install -r requirements.txt` "
        "or, on Insomnia, `uv pip install -r requirements-insomnia.txt`."
    )
    for module_name, reason in checks.items():
        try:
            importlib.import_module(module_name)
        except Exception as exc:  # noqa: BLE001
            failures.append(
                f"- {module_name}: {reason} failed to import "
                f"({exc.__class__.__name__}: {exc})"
            )
    if failures:
        raise RuntimeError(
            "Repo-local PE-family runners require a small AssetOpsBench runtime slice "
            "that is missing or incompatible in the active Python environment:\n"
            + "\n".join(failures)
            + "\n"
            + install_hint
        )
    if sys.version_info < (3, 12):
        _LOG.info(
            "AssetOpsBench declares Python >= 3.12, but the repo-local PE runners "
            "only import its plan-execute subset. Continuing under Python %s.",
            sys.version.split()[0],
        )


def build_llm(model_id: str):
    max_tokens = int(os.environ.get("MAX_TOKENS", "0") or "0")
    if max_tokens > 0:
        return _MaxTokensLiteLLMBackend(model_id=model_id, max_tokens=max_tokens)

    from llm.litellm import LiteLLMBackend

    return LiteLLMBackend(model_id=model_id)


class _MaxTokensLiteLLMBackend:
    """Repo-local LiteLLM wrapper for small-context ablation configs."""

    def __init__(self, model_id: str, max_tokens: int) -> None:
        self._model_id = model_id
        self._max_tokens = max_tokens

    def generate(self, prompt: str, temperature: float = 0.0) -> str:
        return self.generate_with_usage(prompt, temperature).text

    def generate_with_usage(self, prompt: str, temperature: float = 0.0):
        import litellm

        kwargs: dict[str, Any] = {
            "model": self._model_id,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": temperature,
            "max_tokens": self._max_tokens,
        }

        if self._model_id.startswith("watsonx/"):
            kwargs["api_key"] = os.environ["WATSONX_APIKEY"]
            kwargs["project_id"] = os.environ["WATSONX_PROJECT_ID"]
            if url := os.environ.get("WATSONX_URL"):
                kwargs["api_base"] = url
        else:
            kwargs["api_key"] = os.environ["LITELLM_API_KEY"]
            kwargs["api_base"] = os.environ["LITELLM_BASE_URL"]

        response = litellm.completion(**kwargs)
        usage = getattr(response, "usage", None)
        return SimpleNamespace(
            text=response.choices[0].message.content,
            input_tokens=int(getattr(usage, "prompt_tokens", 0) or 0),
            output_tokens=int(getattr(usage, "completion_tokens", 0) or 0),
        )


PLANNING_GUARDRAILS = """\
Planning guardrails for this repo-local Smart Grid setup:
- Use only these real servers: iot, fmsr, tsfm, wo.
- Do not use a fake server like "none" or "null".
- If you only need a final natural-language answer, omit that final step. The
  runner will synthesize the final answer after tool execution.
- get_dga_record and analyze_dga are FMSR tools, not IoT tools.
- get_sensor_correlation expects a failure_mode_id like FM-006, not a
  transformer_id like T-015.
"""

PLANNER_NOTES = {
    "fmsr": [
        "Use get_dga_record(transformer_id=T-xxx) to retrieve the latest DGA snapshot for a transformer.",
        "Use analyze_dga(h2, ch4, c2h2, c2h4, c2h6[, transformer_id]) to diagnose a transformer from gas readings.",
        "Use get_sensor_correlation only when you already know a failure_mode_id like FM-006.",
    ],
    "iot": [
        "IoT tools expose asset metadata and raw sensor/time-series readings only.",
        "IoT does not provide DGA diagnosis tools.",
        "Valid Smart Grid sensor_ids in this repo are load_current_a, oil_temp_c, power_factor, voltage_hv_kv, voltage_lv_kv, and winding_temp_top_c.",
        "There are no DGA gas sensors exposed as IoT time-series sensor_ids.",
    ],
    "tsfm": [
        "TSFM operates on the same sensor_ids exposed by IoT list_sensors.",
        "Use winding_temp_top_c, not winding_temp_c.",
        "Do not use DGA gas names as TSFM sensor_ids.",
    ],
}

SERVER_ALIASES = {
    "i": "iot",
    "iot_server": "iot",
    "f": "fmsr",
    "fmsr_server": "fmsr",
    "t": "tsfm",
    "ts": "tsfm",
    "tsfm_server": "tsfm",
    "w": "wo",
    "wo_server": "wo",
}

TOOL_SCHEMA_HINTS = {
    ("fmsr", "get_sensor_correlation", "failure_mode_id"): (
        "string (failure mode id like FM-006 from list_failure_modes/search_failure_modes; "
        "not a transformer id)"
    ),
    ("fmsr", "get_dga_record", "transformer_id"): "string (transformer id like T-015)",
    (
        "iot",
        "get_asset_metadata",
        "transformer_id",
    ): "string (transformer id like T-015)",
    ("iot", "list_sensors", "transformer_id"): "string (transformer id like T-015)",
    (
        "iot",
        "get_sensor_readings",
        "transformer_id",
    ): "string (transformer id like T-015)",
    ("tsfm", "get_rul", "transformer_id"): "string (transformer id like T-015)",
    ("tsfm", "forecast_rul", "transformer_id"): "string (transformer id like T-015)",
    (
        "tsfm",
        "detect_anomalies",
        "transformer_id",
    ): "string (transformer id like T-015)",
    ("tsfm", "trend_analysis", "transformer_id"): "string (transformer id like T-015)",
}

SENSOR_TOOLS = {"get_sensor_readings", "detect_anomalies", "trend_analysis"}
SENSOR_ALIAS_MAP = {
    "winding_temp_c": "winding_temp_top_c",
}


def parse_server_overrides(entries: list[str]) -> dict[str, Path] | None:
    if not entries:
        return None
    result: dict[str, Path] = {}
    for entry in entries:
        if "=" not in entry:
            raise ValueError(f"--server requires NAME=PATH format, got: {entry!r}")
        name, _, path = entry.partition("=")
        result[name.strip()] = Path(path.strip()).resolve()
    return result


def effective_server_paths(entries: list[str], repo_root: Path) -> dict[str, Path]:
    try:
        overrides = parse_server_overrides(entries)
    except ValueError as exc:
        raise SystemExit(f"Invalid --server entry: {exc}") from exc
    if overrides is not None:
        return overrides
    defaults = {
        "iot": repo_root / "mcp_servers/iot_server/server.py",
        "fmsr": repo_root / "mcp_servers/fmsr_server/server.py",
        "tsfm": repo_root / "mcp_servers/tsfm_server/server.py",
        "wo": repo_root / "mcp_servers/wo_server/server.py",
    }
    env_names = {
        "iot": "SERVER_IOT_PATH",
        "fmsr": "SERVER_FMSR_PATH",
        "tsfm": "SERVER_TSFM_PATH",
        "wo": "SERVER_WO_PATH",
    }
    resolved: dict[str, Path] = {}
    for name, default_path in defaults.items():
        raw = os.environ.get(env_names[name], str(default_path))
        resolved[name] = Path(raw).resolve()
    return resolved


def build_executor(llm, server_paths: dict[str, Path], *, mcp_mode: str = "baseline"):
    """Build the PE-family step executor for the requested MCP mode."""
    if mcp_mode == "baseline":
        from agent.plan_execute.executor import Executor

        return Executor(llm, server_paths)
    if mcp_mode == "optimized":
        return ReusedMCPExecutor(llm, server_paths, resolve_repo_root())
    raise ValueError(f"Unsupported MCP mode for PE-family runner: {mcp_mode!r}")


def load_plan_execute_planner():
    """Load AOB's PE planner with compatibility for common dependency labels."""
    from agent.plan_execute import planner as planner_module

    planner_module._DEP_NUM_RE = re.compile(r"#(?:S|Task)(\d+)")
    return planner_module.Planner


async def close_executor(executor: Any) -> None:
    """Best-effort close hook for executors that keep MCP sessions open."""
    close = getattr(executor, "aclose", None)
    if close is not None:
        await close()


async def build_tool_catalog_for_executor(
    executor: Any,
    server_paths: dict[str, Path],
) -> dict[str, dict[str, dict[str, str]]]:
    """Return tool catalog, using the optimized executor cache when available."""
    if hasattr(executor, "get_tool_catalog"):
        return await executor.get_tool_catalog()
    return await build_tool_catalog(server_paths)


def _server_tool_catalog(
    server_name: str,
    tools: list[dict[str, Any]],
) -> dict[str, dict[str, str]]:
    server_catalog: dict[str, dict[str, str]] = {}
    for tool in tools:
        parameters = []
        for param in tool.get("parameters", []):
            hint = TOOL_SCHEMA_HINTS.get((server_name, tool["name"], param["name"]))
            rendered = hint or f"{param['type']}{'?' if not param['required'] else ''}"
            parameters.append(f"{param['name']}: {rendered}")
        server_catalog[tool["name"]] = {
            "description": tool.get("description", "").strip(),
            "schema": ", ".join(parameters),
        }
    return server_catalog


def _tool_dicts_from_mcp_result(result: Any) -> list[dict[str, Any]]:
    tools = []
    for tool in result.tools:
        schema = tool.inputSchema or {}
        props = schema.get("properties", {})
        required = set(schema.get("required", []))
        parameters = [
            {
                "name": key,
                "type": value.get("type", "any"),
                "required": key in required,
            }
            for key, value in props.items()
        ]
        tools.append(
            {
                "name": tool.name,
                "description": tool.description or "",
                "parameters": parameters,
            }
        )
    return tools


def _optimized_stdio_params(repo_root: Path, server_path: Path):
    """Build stdio params using the same warmed bootstrap as AaT MCP runs."""
    from mcp import StdioServerParameters

    bootstrap = repo_root / "scripts" / "aat_mcp_server_bootstrap.py"
    if not bootstrap.exists():
        raise FileNotFoundError(f"MCP server bootstrap missing: {bootstrap}")

    launch_mode = os.environ.get("AAT_MCP_SERVER_LAUNCH_MODE", "python").lower()
    env = {**os.environ, "PYTHONUNBUFFERED": "1", "AAT_MCP_REPO_ROOT": str(repo_root)}

    if launch_mode == "uv":
        deps = ["mcp[cli]==1.27.0", "pandas", "numpy"]
        return StdioServerParameters(
            command="uv",
            args=[
                "run",
                *(arg for dep in deps for arg in ("--with", dep)),
                "python",
                "-u",
                str(bootstrap),
                str(server_path),
            ],
            cwd=str(repo_root),
            env=env,
        )
    if launch_mode != "python":
        raise ValueError(
            "AAT_MCP_SERVER_LAUNCH_MODE must be either 'python' or 'uv', "
            f"got {launch_mode!r}"
        )

    server_python = os.environ.get("AAT_MCP_SERVER_PYTHON") or sys.executable
    return StdioServerParameters(
        command=server_python,
        args=["-u", str(bootstrap), str(server_path)],
        cwd=str(repo_root),
        env=env,
    )


class ReusedMCPExecutor:
    """PE-family executor that reuses initialized MCP stdio sessions.

    AOB's baseline Executor opens a fresh stdio MCP client for every list-tools
    and call-tool operation. This variant keeps one initialized session per
    server for a whole scenario run, which is the PE-family analogue of the
    Cell C connection-reuse optimization.
    """

    def __init__(self, llm, server_paths: dict[str, Path], repo_root: Path) -> None:
        self._llm = llm
        self._server_paths = server_paths
        self._repo_root = repo_root
        self._stack = AsyncExitStack()
        self._sessions: dict[str, Any] = {}
        self._tool_cache: dict[str, list[dict[str, Any]]] = {}

    async def aclose(self) -> None:
        await self._stack.aclose()
        self._sessions.clear()
        self._tool_cache.clear()

    async def _session(self, server_name: str):
        if server_name in self._sessions:
            return self._sessions[server_name]
        from mcp import ClientSession
        from mcp.client.stdio import stdio_client

        server_path = self._server_paths[server_name]
        params = _optimized_stdio_params(self._repo_root, Path(server_path).resolve())
        read, write = await self._stack.enter_async_context(stdio_client(params))
        timeout_seconds = float(os.environ.get("AAT_MCP_CLIENT_TIMEOUT_SECONDS", "120"))
        session = await self._stack.enter_async_context(
            ClientSession(
                read,
                write,
                read_timeout_seconds=timedelta(seconds=timeout_seconds),
            )
        )
        await session.initialize()
        self._sessions[server_name] = session
        _LOG.info(
            "Connected optimized MCP server %s via %s", server_name, params.command
        )
        return session

    async def _tools(self, server_name: str) -> list[dict[str, Any]]:
        if server_name not in self._tool_cache:
            session = await self._session(server_name)
            result = await session.list_tools()
            self._tool_cache[server_name] = _tool_dicts_from_mcp_result(result)
        return self._tool_cache[server_name]

    async def get_tool_catalog(self) -> dict[str, dict[str, dict[str, str]]]:
        catalog: dict[str, dict[str, dict[str, str]]] = {}
        for server_name in self._server_paths:
            catalog[server_name] = _server_tool_catalog(
                server_name,
                await self._tools(server_name),
            )
        return catalog

    async def get_server_descriptions(self) -> dict[str, str]:
        descriptions: dict[str, str] = {}
        for server_name in self._server_paths:
            tools = await self._tools(server_name)
            lines = []
            for tool in tools:
                params = ", ".join(
                    f"{p['name']}: {p['type']}{'?' if not p['required'] else ''}"
                    for p in tool.get("parameters", [])
                )
                lines.append(f"  - {tool['name']}({params}): {tool['description']}")
            descriptions[server_name] = "\n".join(lines)
        return descriptions

    async def execute_step(
        self,
        step,
        context: dict[int, Any],
        question: str,
        tool_schema: str = "",
    ):
        from agent.plan_execute.executor import _extract_content, _resolve_args_with_llm
        from agent.plan_execute.models import StepResult

        if step.server not in self._server_paths:
            return StepResult(
                step_number=step.step_number,
                task=step.task,
                server=step.server,
                response="",
                error=(
                    f"Unknown server '{step.server}'. "
                    f"Registered servers: {list(self._server_paths)}"
                ),
            )

        if not step.tool or step.tool.lower() in ("none", "null"):
            return StepResult(
                step_number=step.step_number,
                task=step.task,
                server=step.server,
                response=step.expected_output,
                tool=step.tool,
                tool_args=step.tool_args,
            )

        try:
            resolved_args = await _resolve_args_with_llm(
                question,
                step.task,
                step.tool,
                tool_schema,
                context,
                self._llm,
            )
            session = await self._session(step.server)
            result = await session.call_tool(step.tool, resolved_args)
            return StepResult(
                step_number=step.step_number,
                task=step.task,
                server=step.server,
                response=_extract_content(result.content),
                tool=step.tool,
                tool_args=resolved_args,
            )
        except Exception as exc:  # noqa: BLE001
            return StepResult(
                step_number=step.step_number,
                task=step.task,
                server=step.server,
                response="",
                error=str(exc),
                tool=step.tool,
                tool_args=step.tool_args,
            )


def build_planning_question(question: str) -> str:
    return question.strip() + "\n\n" + PLANNING_GUARDRAILS.strip()


async def build_tool_catalog(
    server_paths: dict[str, Path],
) -> dict[str, dict[str, dict[str, str]]]:
    from agent.plan_execute.executor import _list_tools

    catalog: dict[str, dict[str, dict[str, str]]] = {}
    for name, path in server_paths.items():
        try:
            tools = await _list_tools(path)
        except Exception:  # noqa: BLE001
            catalog[name] = {}
            continue
        catalog[name] = _server_tool_catalog(name, tools)
    return catalog


def build_planner_descriptions(
    descriptions: dict[str, str],
    tool_catalog: dict[str, dict[str, dict[str, str]]],
) -> dict[str, str]:
    planner_descriptions: dict[str, str] = {}
    for server_name, description in descriptions.items():
        lines = [description.strip()] if description.strip() else []
        notes = PLANNER_NOTES.get(server_name, [])
        if notes:
            lines.append("Planner notes:")
            lines.extend(f"  - {note}" for note in notes)
        if server_name in tool_catalog and tool_catalog[server_name]:
            lines.append("Canonical tool signatures:")
            for tool_name, info in tool_catalog[server_name].items():
                schema = info.get("schema", "")
                desc = info.get("description", "")
                lines.append(f"  - {tool_name}({schema}): {desc}".rstrip())
        planner_descriptions[server_name] = "\n".join(line for line in lines if line)
    return planner_descriptions


def normalize_plan_steps(
    plan,
    tool_catalog: dict[str, dict[str, dict[str, str]]],
    *,
    default_server: str = "iot",
) -> list[str]:
    warnings: list[str] = []
    if not getattr(plan, "steps", None):
        return warnings

    tool_servers: dict[str, set[str]] = {}
    for server_name, tools in tool_catalog.items():
        for tool_name in tools:
            tool_servers.setdefault(tool_name, set()).add(server_name)

    ordered_steps = sorted(plan.steps, key=lambda step: int(step.step_number))
    kept_steps = []
    for index, step in enumerate(ordered_steps):
        server = str(getattr(step, "server", "") or "").strip()
        tool = str(getattr(step, "tool", "") or "").strip()
        server_key = server.lower()
        tool_key = tool
        is_terminal = index == len(ordered_steps) - 1

        if server_key in SERVER_ALIASES:
            normalized_server = SERVER_ALIASES[server_key]
            setattr(step, "server", normalized_server)
            server_key = normalized_server
            warnings.append(
                f"Normalized server alias {server!r} to {normalized_server} for step {step.step_number}."
            )

        if tool_key.lower() in {"", "none", "null"}:
            if server_key in {"", "none", "null"} and is_terminal:
                warnings.append(
                    f"Dropped terminal synthesis-only step {step.step_number}; final answer is summarized outside the plan."
                )
                continue
            if server_key in {"", "none", "null"}:
                setattr(step, "server", default_server)
                warnings.append(
                    f"Normalized server for no-tool step {step.step_number} to {default_server}."
                )
            kept_steps.append(step)
            continue

        if server_key in {"", "none", "null"} or server_key not in tool_catalog:
            candidates = sorted(tool_servers.get(tool_key, set()))
            if len(candidates) == 1:
                setattr(step, "server", candidates[0])
                warnings.append(
                    f"Rerouted step {step.step_number} tool {tool_key} to server {candidates[0]}."
                )
                kept_steps.append(step)
                continue

        elif tool_key not in tool_catalog.get(server_key, {}):
            candidates = sorted(tool_servers.get(tool_key, set()))
            if len(candidates) == 1:
                setattr(step, "server", candidates[0])
                warnings.append(
                    f"Rerouted step {step.step_number} tool {tool_key} from {server_key} to {candidates[0]}."
                )
                kept_steps.append(step)
                continue

        kept_steps.append(step)

    old_to_new: dict[int, int] = {}
    for new_step_number, step in enumerate(kept_steps, start=1):
        old_to_new[int(step.step_number)] = new_step_number
        setattr(step, "step_number", new_step_number)

    for step in kept_steps:
        dependencies = [
            old_to_new[dep]
            for dep in getattr(step, "dependencies", [])
            if dep in old_to_new
        ]
        setattr(step, "dependencies", dependencies)

    plan.steps = kept_steps
    return warnings


def tool_schema_for_step(
    tool_catalog: dict[str, dict[str, dict[str, str]]],
    server: str,
    tool: str,
) -> str:
    return tool_catalog.get(server, {}).get(tool, {}).get("schema", "")


def parse_json_object(raw: str) -> dict[str, Any]:
    text = raw.strip()
    if text.startswith("```"):
        lines = text.splitlines()
        inner = lines[1:-1] if lines and lines[-1].strip() == "```" else lines[1:]
        text = "\n".join(inner).strip()
    try:
        value = json.loads(text)
        return value if isinstance(value, dict) else {}
    except json.JSONDecodeError:
        start = text.find("{")
        end = text.rfind("}") + 1
        if start != -1 and end > start:
            try:
                value = json.loads(text[start:end])
                return value if isinstance(value, dict) else {}
            except json.JSONDecodeError:
                return {}
        return {}


def parse_json_like_value(raw: Any) -> Any:
    if not isinstance(raw, str):
        return raw

    text = raw.strip()
    if not text:
        return raw

    try:
        payload = json.loads(text)
        if isinstance(payload, (dict, list)):
            return payload
    except json.JSONDecodeError:
        pass

    payload = parse_json_object(text)
    if payload:
        return payload

    lines = [line.strip() for line in text.splitlines() if line.strip()]
    if not lines:
        return raw

    parsed_items = []
    for line in lines:
        item = parse_json_object(line)
        if not item:
            return raw
        parsed_items.append(item)
    return parsed_items


def maybe_self_ask(question: str, llm) -> SelfAskDecision:
    raw = llm.generate(SELF_ASK_PROMPT.format(question=question))
    payload = parse_json_object(raw)
    needs_self_ask = bool(payload.get("needs_self_ask", False))
    clarifying_questions = [
        str(item).strip()
        for item in payload.get("clarifying_questions", [])
        if str(item).strip()
    ][:2]
    assumptions = [
        str(item).strip()
        for item in payload.get("assumptions", [])
        if str(item).strip()
    ][:3]
    augmented_question = str(payload.get("augmented_question", "")).strip()
    if not needs_self_ask:
        return SelfAskDecision(
            needs_self_ask=False,
            clarifying_questions=[],
            assumptions=[],
            augmented_question=question,
        )
    if not augmented_question:
        extra = []
        if clarifying_questions:
            extra.append(
                "Resolve these clarification questions internally before answering:\n- "
                + "\n- ".join(clarifying_questions)
            )
        if assumptions:
            extra.append(
                "Use these temporary assumptions if needed:\n- "
                + "\n- ".join(assumptions)
            )
        augmented_question = question + "\n\n" + "\n\n".join(extra)
    return SelfAskDecision(
        needs_self_ask=True,
        clarifying_questions=clarifying_questions,
        assumptions=assumptions,
        augmented_question=augmented_question,
    )


def terminal_history(history: list[dict[str, Any]]) -> list[dict[str, Any]]:
    latest_by_step: dict[int, dict[str, Any]] = {}
    for entry in history:
        latest_by_step[int(entry["step"])] = entry
    return [latest_by_step[step] for step in sorted(latest_by_step)]


def summarize_terminal_failures(history: list[dict[str, Any]]) -> list[dict[str, Any]]:
    failures = []
    for entry in terminal_history(history):
        if bool(entry.get("success", False)):
            continue
        failures.append(
            {
                "step": int(entry["step"]),
                "task": entry.get("task"),
                "server": entry.get("server"),
                "tool": entry.get("tool"),
                "error": entry.get("error"),
                "verifier_decision": entry.get("verifier_decision"),
            }
        )
    return failures


def summarize_answer(
    question: str,
    history: list[dict[str, Any]],
    llm,
    *,
    fault_risk_adjudication: dict[str, Any] | None = None,
) -> str:
    if (
        fault_risk_adjudication
        and fault_risk_adjudication.get("decision") == "refuse_due_missing_evidence"
    ):
        from mitigation_guards import adjudication_refusal_answer

        return adjudication_refusal_answer(fault_risk_adjudication)

    final_history = terminal_history(history)
    results_text = "\n\n".join(
        f"Step {entry['step']} - {entry['task']} (server: {entry['server']}):\n"
        + (
            compact_prompt_text(entry["response"], limit=600)
            if entry["success"]
            else f"ERROR: {compact_prompt_text(entry['error'], limit=240)}"
        )
        for entry in final_history
    )
    if (
        fault_risk_adjudication
        and fault_risk_adjudication.get("decision") == "finalize"
    ):
        results_text += SUMMARIZE_ADJUDICATION_BLOCK.format(
            adjudication=json.dumps(fault_risk_adjudication, indent=2)
        )
    try:
        return llm.generate(
            SUMMARIZE_PROMPT.format(question=question, results=results_text)
        )
    except Exception as exc:
        _LOG.warning(
            "Final answer summarization failed (%s); using a compact fallback summary.",
            exc,
        )
        parts = []
        for entry in final_history:
            detail = (
                compact_prompt_text(entry["response"], limit=160)
                if entry["success"]
                else f"ERROR: {compact_prompt_text(entry['error'], limit=120)}"
            )
            parts.append(f"Step {entry['step']} ({entry['tool']}): {detail}")
        return (
            f"Question: {question}\n"
            "Execution completed, but the LLM summary step exceeded the prompt budget. "
            "Compact terminal results:\n- " + "\n- ".join(parts)
        )


def serialize_steps(steps: list[Any]) -> list[dict[str, Any]]:
    return [
        {
            "step": step.step_number,
            "task": step.task,
            "server": step.server,
            "tool": step.tool,
            "tool_args": step.tool_args,
            "dependencies": step.dependencies,
            "expected_output": step.expected_output,
        }
        for step in steps
    ]


def serialize_plan(plan) -> list[dict[str, Any]]:
    return serialize_steps(plan.steps)


def response_error_payload(response: Any) -> str | None:
    response = parse_json_like_value(response)
    if isinstance(response, dict):
        # In runner artifacts, a top-level "error" field is treated as an
        # operational failure signal rather than ordinary payload content.
        value = response.get("error")
        if value:
            return str(value).strip()
        return None

    if isinstance(response, list):
        for item in response:
            value = response_error_payload(item)
            if value:
                return value
        return None

    if not isinstance(response, str):
        return None

    text = response.strip()
    if not text:
        return None
    if text.lower().startswith("unknown tool:"):
        return text
    payload = parse_json_object(text)
    return response_error_payload(payload)


def serialize_step_result(result, **extra: Any) -> dict[str, Any]:
    normalized_response = normalize_response_text(result.response)
    payload_error = response_error_payload(normalized_response)
    payload = {
        "step": result.step_number,
        "task": result.task,
        "server": result.server,
        "tool": result.tool,
        "tool_args": result.tool_args,
        "response": normalized_response,
        "executor_success": bool(result.success),
        "error": payload_error or result.error,
        "success": bool(result.success) and payload_error is None,
    }
    for optional_field in ("runner_repair", "runner_repair_reason"):
        if hasattr(result, optional_field):
            payload[optional_field] = getattr(result, optional_field)
    payload.update(extra)
    return payload


def normalize_response_text(response: Any) -> str:
    parsed = parse_json_like_value(response)
    if isinstance(parsed, (dict, list)):
        return json.dumps(parsed, indent=2)
    return str(response)


def canonicalize_step_result(result) -> None:
    result.response = normalize_response_text(result.response)


def compact_step_for_context(result) -> None:
    response = getattr(result, "response", None)
    if response is not None:
        result.response = compact_prompt_text(
            response, limit=MAX_CONTEXT_RESPONSE_CHARS
        )
    error = getattr(result, "error", None)
    if error:
        result.error = compact_prompt_text(error, limit=MAX_CONTEXT_ERROR_CHARS)


def available_sensor_ids(context: dict[int, Any]) -> set[str]:
    sensors: set[str] = set()
    for result in context.values():
        if getattr(result, "tool", "") != "list_sensors":
            continue
        payload = parse_json_like_value(getattr(result, "response", ""))
        if isinstance(payload, dict):
            sensor_id = payload.get("sensor_id")
            if sensor_id:
                sensors.add(str(sensor_id))
        elif isinstance(payload, list):
            for item in payload:
                if isinstance(item, dict) and item.get("sensor_id"):
                    sensors.add(str(item["sensor_id"]))
    return sensors


def extract_requested_sensor(task: str) -> str | None:
    patterns = [
        r"([A-Za-z0-9_]+)\s+sensor(?:'s)?\s+readings",
        r"sensor\s+([A-Za-z0-9_]+)\b",
    ]
    for pattern in patterns:
        match = re.search(pattern, task)
        if match:
            return match.group(1)
    return None


def repair_sensor_task_text(task: str, sensors: set[str]) -> tuple[str, str | None]:
    requested = extract_requested_sensor(task)
    if not requested or requested in sensors:
        return task, None

    alias = SENSOR_ALIAS_MAP.get(requested)
    if alias and alias in sensors:
        return task.replace(requested, alias), (
            f"Rewrote sensor id {requested} -> {alias} from list_sensors context."
        )

    return task, None


def should_skip_invalid_sensor_step(step: Any, sensors: set[str]) -> tuple[bool, str]:
    if getattr(step, "tool", "") != "get_sensor_readings":
        return False, ""

    requested = extract_requested_sensor(getattr(step, "task", ""))
    if not requested or requested in sensors:
        return False, ""

    lowered = requested.lower()
    if lowered.startswith("dga_") or lowered.startswith("dissolved_"):
        return (
            True,
            (
                f"Skipped unsupported IoT sensor lookup for {requested}; "
                "DGA data in this repo comes from FMSR get_dga_record/analyze_dga, "
                "not IoT time-series sensors."
            ),
        )

    return False, ""


def compact_history(history: list[dict[str, Any]]) -> str:
    if not history:
        return "(none)"
    chunks = []
    for entry in history:
        status = "OK" if entry["success"] else "ERR"
        detail = entry["response"] if entry["success"] else entry["error"]
        detail = compact_prompt_text(detail, limit=240)
        chunks.append(
            f"Step {entry['step']} [{status}] {entry['task']} | tool={entry['tool']} | detail={detail}"
        )
    return "\n".join(chunks)


def compact_steps(steps: list[Any]) -> str:
    if not steps:
        return "(none)"
    return "\n".join(
        f"Step {step.step_number}: {step.task} | server={step.server} | tool={step.tool}"
        for step in steps
    )


def compact_prompt_text(value: Any, *, limit: int) -> str:
    text = normalize_response_text(value)
    if len(text) <= limit:
        return text
    truncated = len(text) - limit
    return f"{text[:limit]}\n...[truncated {truncated} chars]"


def compact_verifier_result(current_result: dict[str, Any]) -> dict[str, Any]:
    compacted = dict(current_result)
    if "response" in compacted:
        compacted["response"] = compact_prompt_text(compacted["response"], limit=1200)
    if "error" in compacted and compacted["error"]:
        compacted["error"] = compact_prompt_text(compacted["error"], limit=600)
    return compacted


def verify_step(
    question: str,
    effective_question: str,
    current_step: Any,
    current_result: dict[str, Any],
    history: list[dict[str, Any]],
    remaining_steps: list[Any],
    llm,
) -> VerificationDecision:
    verifier_payload = compact_verifier_result(current_result)
    try:
        raw = llm.generate(
            VERIFIER_PROMPT.format(
                question=question,
                effective_question=effective_question,
                current_step=(
                    f"Step {current_step.step_number}: {current_step.task} "
                    f"(server={current_step.server}, tool={current_step.tool})"
                ),
                current_result=json.dumps(verifier_payload, indent=2),
                history=compact_history(history),
                remaining_steps=compact_steps(remaining_steps),
            )
        )
    except Exception as exc:
        _LOG.warning(
            "Verifier failed (%s); falling back to decision='continue'.",
            exc,
        )
        return VerificationDecision(
            decision="continue",
            reason="Verifier unavailable; continuing with the current result.",
            updated_focus="",
        )
    payload = parse_json_object(raw)
    if not payload:
        _LOG.warning(
            "Verifier returned no parseable JSON; falling back to decision='continue'."
        )
    decision = str(payload.get("decision", "continue")).strip().lower()
    if decision not in {"continue", "retry", "replan_suffix"}:
        _LOG.warning(
            "Verifier returned unknown decision %r; falling back to 'continue'.",
            decision,
        )
        decision = "continue"
    return VerificationDecision(
        decision=decision,
        reason=str(payload.get("reason", "")).strip()
        or "No additional verifier reason recorded.",
        updated_focus=str(payload.get("updated_focus", "")).strip(),
    )


def build_retry_question(
    question: str,
    effective_question: str,
    current_step: Any,
    current_result: dict[str, Any],
    decision: VerificationDecision,
    retries_used: int,
) -> str:
    prompt = [
        effective_question,
        "",
        "Retry guidance for the current execution step:",
        f"- Original question: {question}",
        f"- Step: {current_step.step_number} / {current_step.task}",
        (
            "- Previous attempt result: "
            f"{compact_prompt_text(current_result.get('response') or current_result.get('error') or '(none)', limit=400)}"
        ),
        f"- Verifier reason: {decision.reason}",
        f"- Retry attempt number: {retries_used}",
        "- Keep the same overall intent, but correct the specific issue the verifier flagged.",
    ]
    if decision.updated_focus:
        prompt.append(f"- Updated focus: {decision.updated_focus}")
    return "\n".join(prompt)


def build_suffix_replan_question(
    question: str,
    effective_question: str,
    history: list[dict[str, Any]],
    remaining_steps: list[Any],
    decision: VerificationDecision,
) -> str:
    prompt = [
        "Replan only the remaining suffix of the task.",
        "",
        f"Original question:\n{question}",
        "",
        f"Effective planning question:\n{effective_question}",
        "",
        "Verified completed context:",
        compact_history(history),
        "",
        "Remaining plan that may need repair:",
        compact_steps(remaining_steps),
        "",
        f"Verifier reason: {decision.reason}",
    ]
    if decision.updated_focus:
        prompt.extend(["", f"Updated focus for the suffix: {decision.updated_focus}"])
    prompt.extend(
        [
            "",
            "Only plan the remaining work. Do not repeat already completed steps.",
            "Rules for this repaired suffix plan:",
            "- Start the repaired suffix at step 1.",
            "- Completed steps are already done and available as context, not as dependencies.",
            "- The first suffix step must use Dependency1: None.",
            "- Only reference dependencies on earlier suffix steps (#S1, #S2, ...) and never on completed steps.",
            "- If a suffix step needs prior completed context, mention it in the task text instead of as a dependency.",
        ]
    )
    return "\n".join(prompt)


def generate_suffix_plan(planner, replan_question: str, descriptions: dict[str, str]):
    try:
        return planner.generate_plan(replan_question, descriptions), None
    except Exception as exc:  # noqa: BLE001
        _LOG.warning(
            "Suffix replan generation failed; continuing with the original remaining plan: %s",
            exc,
        )
        return None, str(exc)


def renumber_plan(plan, offset: int):
    from agent.plan_execute.models import Plan, PlanStep

    renumbered_steps = []
    for step in plan.steps:
        renumbered_steps.append(
            PlanStep(
                step_number=step.step_number + offset,
                task=step.task,
                server=step.server,
                tool=step.tool,
                tool_args=step.tool_args,
                dependencies=[dep + offset for dep in step.dependencies],
                expected_output=step.expected_output,
            )
        )
    return Plan(steps=renumbered_steps, raw=plan.raw)


def print_plan(plan: list[dict[str, Any]]) -> None:
    print("\n" + "─" * 60)
    print("  Plan")
    print("─" * 60)
    for step in plan:
        deps = ", ".join(f"#{dep}" for dep in step["dependencies"]) or "none"
        print(f"  [{step['step']}] {step['server']}: {step['task']}")
        print(f"       tool: {step['tool']}  args: {step['tool_args']}")
        print(f"       deps={deps} | expected: {step['expected_output']}")


def print_history(history: list[dict[str, Any]]) -> None:
    print("\n" + "─" * 60)
    print("  Trajectory")
    print("─" * 60)
    for entry in history:
        status = "OK " if entry["success"] else "ERR"
        print(f"  [{status}] Step {entry['step']} ({entry['server']}): {entry['task']}")
        if entry["tool"] and entry["tool"].lower() not in {"none", "null", ""}:
            print(f"       tool: {entry['tool']}  args: {entry['tool_args']}")
        if entry.get("verifier_decision"):
            print(
                "       verifier: "
                f"{entry['verifier_decision']} | {entry.get('verifier_reason', '')}"
            )
        detail = entry["response"] if entry["success"] else f"Error: {entry['error']}"
        snippet = detail[:200] + ("..." if len(detail) > 200 else "")
        print(f"        {snippet}")