odysseus/src/tool_execution.py

"""
tool_execution.py

Tool dispatcher and result formatter for the agent loop.
Routes tool blocks to MCP servers or native implementations.

Extracted from agent_tools.py.
"""

import asyncio
import collections
import contextvars
import json
import logging
import os
import pathlib
import re
import sys
import time
from typing import Any, Awaitable, Callable, Dict, Optional, Tuple



from src.tool_security import is_public_blocked_tool, owner_is_admin_or_single_user
from src.tool_policy import ToolPolicy
from src.constants import MAX_OUTPUT_CHARS, MAX_READ_CHARS, MAX_DIFF_LINES, DATA_DIR
from src.tool_utils import _truncate, get_mcp_manager

# Persistent working directory for agent subprocesses.
# Resolves to <repo_root>/data, which is the bind-mounted volume in Docker
# (/app/data) and the local data directory for manual installs.
# Using this as cwd and HOME prevents the agent from silently creating files
# in ephemeral container layers that are lost on the next rebuild.
_AGENT_WORKDIR = DATA_DIR



# ---------------------------------------------------------------------------
# Path confinement for read_file / write_file
# ---------------------------------------------------------------------------
# read_file + write_file are admin-only tools, but the path the agent
# supplies is model-controlled. Prompt-injection in an admin's chat can
# weaponise "read /etc/shadow" or "write ~/.ssh/authorized_keys" without
# the admin noticing.
#
# Policy:
#   1. Sensitive-subpath deny list — checked FIRST. Blocks .ssh,
#      .gnupg, shell rc files, token/env files even if the root above
#      them is on the allowlist.
#   2. Allowlist — only the directories the agent legitimately needs
#      (project data/, system tmp). $HOME is NOT on the default list.
#   3. Opt-in extra roots — admin can add broader roots via the
#      "tool_path_extra_roots" setting (list of path strings).
# ---------------------------------------------------------------------------

_SENSITIVE_BASENAMES: set[str] = {
    ".ssh", ".gnupg", ".gitconfig",
    ".bashrc", ".bash_profile", ".bash_logout",
    ".zshrc", ".zprofile", ".zshenv",
    ".profile", ".tcshrc", ".cshrc",
    ".env", ".netrc",
}

_SENSITIVE_FILE_PATTERNS: tuple[str, ...] = (
    "authorized_keys", "id_rsa", "id_ed25519", "id_ecdsa",
    "known_hosts",
)


def _is_sensitive_path(resolved: str) -> bool:
    """Return True if *resolved* falls under a sensitive directory or
    matches a sensitive filename — regardless of what root it sits under.
    """
    parts = resolved.split(os.sep)
    filenames: set[str] = {parts[-1]} if parts else set()

    # Check if any path component is a sensitive directory.
    for part in parts:
        if part in _SENSITIVE_BASENAMES:
            return True

    # Check filename against known sensitive files.
    for pat in _SENSITIVE_FILE_PATTERNS:
        if pat in filenames:
            return True

    return False


def _tool_path_roots() -> list[str]:
    """Return the list of directory roots that read_file / write_file
    may touch. Default: project data/ + system temp dirs. Extra roots
    are loaded from the ``tool_path_extra_roots`` setting.
    """
    roots: list[str] = []

    # Project data directory — the agent's primary workspace.
    from src.constants import DATA_DIR
    roots.append(DATA_DIR)

    # /tmp (and its macOS realpath /private/tmp).
    roots.append("/tmp")
    try:
        private_tmp = os.path.realpath("/tmp")
        if private_tmp != "/tmp":
            roots.append(private_tmp)
    except OSError:
        pass

    # $TMPDIR — per-user temp root on macOS (e.g. /var/folders/.../T/).
    tmpdir = os.environ.get("TMPDIR")
    if tmpdir:
        roots.append(tmpdir)

    # Opt-in extra roots from settings.
    try:
        from src.settings import get_setting
        extra = get_setting("tool_path_extra_roots")
        if isinstance(extra, list):
            roots.extend(str(r) for r in extra if r)
    except Exception:
        pass

    # Deduplicate; resolve symlinks so containment is unambiguous.
    seen: set[str] = set()
    out: list[str] = []
    for r in roots:
        try:
            real = os.path.realpath(r)
        except OSError:
            continue
        if real in seen:
            continue
        seen.add(real)
        out.append(real)
    return out


def _resolve_tool_path(raw_path: str) -> str:
    """Resolve and confine a model-supplied path.

    Order of checks:
      1. Non-empty path.
      2. Sensitive-subpath deny list (blocks .ssh, .gnupg, etc.
         even when the root is on the allowlist).
      3. Allowlist containment (must land under one of the roots).

    Returns the realpath on success. Raises ValueError on rejection.
    Symlinks are resolved before comparison.

    When a workspace is active for this turn, paths are confined to it instead
    of the default allowlist (see _resolve_tool_path_in_workspace).
    """
    ws = get_active_workspace()
    if ws:
        return _resolve_tool_path_in_workspace(ws, raw_path)
    if raw_path is None or not str(raw_path).strip():
        raise ValueError("path is required")
    expanded = os.path.expanduser(str(raw_path).strip())
    resolved = os.path.realpath(expanded)

    if _is_sensitive_path(resolved):
        raise ValueError(
            f"path '{raw_path}' is inside a sensitive directory "
            f"(e.g. .ssh, .gnupg) or matches a sensitive filename"
        )

    for root in _tool_path_roots():
        if resolved == root:
            return resolved
        try:
            common = os.path.commonpath([resolved, root])
        except ValueError:
            continue
        if common == root:
            return resolved
    raise ValueError(
        f"path '{raw_path}' is outside the allowed roots"
    )


def _resolve_tool_path_in_workspace(workspace: str, raw_path: str) -> str:
    """Confine a model-supplied path to the active workspace.

    Layered on top of upstream's path policy: the workspace is the allowed
    root (relative paths resolve under it; paths that escape it are rejected),
    and the sensitive-file deny list (.ssh, .gnupg, id_rsa, …) still applies
    inside it. When no workspace is set, callers use _resolve_tool_path (the
    default data/tmp allowlist) instead.
    """
    if raw_path is None or not str(raw_path).strip():
        raise ValueError("path is required")
    base = os.path.realpath(workspace)
    expanded = os.path.expanduser(str(raw_path).strip())
    candidate = expanded if os.path.isabs(expanded) else os.path.join(base, expanded)
    resolved = os.path.realpath(candidate)
    if _is_sensitive_path(resolved):
        raise ValueError(
            f"path '{raw_path}' is inside a sensitive directory "
            f"(e.g. .ssh, .gnupg) or matches a sensitive filename"
        )
    if resolved != base:
        # normcase so containment holds on case-insensitive filesystems
        # (Windows, default macOS): it lowercases on Windows and is a no-op on
        # POSIX. commonpath raises ValueError across Windows drives (C: vs D:)
        # or mixed abs/rel — both mean "outside", so the except rejects them.
        nbase = os.path.normcase(base)
        try:
            if os.path.commonpath([os.path.normcase(resolved), nbase]) != nbase:
                raise ValueError
        except ValueError:
            raise ValueError(f"path '{raw_path}' is outside the workspace ({workspace})")
    return resolved



# ---------------------------------------------------------------------------
# Active workspace (per-turn, context-local)
# ---------------------------------------------------------------------------
# Set ONCE in execute_tool_block from the request's `workspace`. The path
# resolvers (_resolve_tool_path / _resolve_search_root) and the subprocess cwd
# helper (agent_cwd) read it from here, so confinement is enforced in a single
# place: any tool that resolves paths through these helpers is confined
# automatically and cannot accidentally bypass the workspace. contextvars are
# task-local, so concurrent turns don't leak into each other.
_active_workspace: contextvars.ContextVar = contextvars.ContextVar(
    "agent_active_workspace", default=None
)


def get_active_workspace() -> Optional[str]:
    """The folder the agent is confined to this turn, or None."""
    return _active_workspace.get()


def vet_workspace(raw: str) -> Optional[str]:
    """Validate a requested workspace path at bind time.

    Returns the canonical path, or None when it is unusable: not a real
    directory, or itself a sensitive path (.ssh, .gnupg, ...). The in-workspace
    resolver deny-lists sensitive paths *inside* the workspace, but the
    empty-path search root is the workspace itself, so the root has to be
    vetted before it is ever bound.
    """
    raw = (raw or "").strip()
    if not raw:
        return None
    resolved = os.path.realpath(os.path.expanduser(raw))
    if not os.path.isdir(resolved) or _is_sensitive_path(resolved):
        return None
    # Reject filesystem roots: binding / (or a Windows drive/UNC root) as the
    # workspace would make every absolute path "inside" it, collapsing the
    # confinement into host-wide file access. A root is its own dirname, which
    # also covers C:\ and \\server\share without platform-specific lists.
    if os.path.dirname(resolved) == resolved:
        return None
    return resolved


def agent_cwd() -> str:
    """Working directory for agent subprocesses (bash/python/background jobs):
    the active workspace when set, else the persistent data dir."""
    return get_active_workspace() or _AGENT_WORKDIR


def get_mcp_manager():
    from src import agent_tools
    return agent_tools.get_mcp_manager()




def _resolve_search_root(raw_path: str) -> str:
    """Resolve + confine a code-nav path (grep/glob/ls).

    With a workspace active, the workspace folder is the root and a supplied
    path is confined inside it. Otherwise an empty path defaults to the agent's
    primary root (project data dir) and a supplied path is confined by the
    global allowlist + sensitive-file policy.
    """
    raw = (raw_path or "").strip()
    ws = get_active_workspace()
    if ws:
        return os.path.realpath(ws) if not raw else _resolve_tool_path_in_workspace(ws, raw)
    if not raw:
        roots = _tool_path_roots()
        return roots[0] if roots else os.path.realpath(".")
    return _resolve_tool_path(raw)

logger = logging.getLogger(__name__)


_ADMIN_TOOLS = {
    "app_api",
    "manage_endpoints",
    "manage_mcp",
    "manage_webhooks",
    "manage_tokens",
    "manage_settings",
    "download_model",
    "serve_model",
    "serve_preset",
    "stop_served_model",
    "cancel_download",
}


def _owner_is_admin(owner: Optional[str]) -> bool:
    """Mirror route-level admin behavior for agent tool execution."""
    return owner_is_admin_or_single_user(owner)

# ---------------------------------------------------------------------------
# MCP-backed tool helpers
# ---------------------------------------------------------------------------

# Map legacy tool names -> (MCP server_id, MCP tool_name)
_MCP_TOOL_MAP = {
    "bash":           ("bash",       "bash"),
    "python":         ("python",     "python"),
    "read_file":      ("filesystem", "read_file"),
    "write_file":     ("filesystem", "write_file"),
    "web_search":     ("web_search", "web_search"),
    "web_fetch":      ("web_fetch",  "web_fetch"),
    "generate_image": ("image_gen",  "generate_image"),
}


def _parse_generate_image(content: str) -> Dict:
    lines = content.strip().split("\n")
    args = {"prompt": lines[0].strip() if lines else ""}
    for i, key in enumerate(["model", "size", "quality"], 1):
        if len(lines) > i and lines[i].strip():
            args[key] = lines[i].strip()
    return args


def _parse_manage_memory(content: str) -> Dict:
    lines = content.strip().split("\n")
    action = lines[0].strip().lower() if lines else ""
    args = {"action": action}
    if action == "add":
        args["text"] = lines[1].strip() if len(lines) > 1 else ""
        if len(lines) > 2 and lines[2].strip():
            args["category"] = lines[2].strip().lower()
    elif action == "edit":
        args["memory_id"] = lines[1].strip() if len(lines) > 1 else ""
        args["text"] = lines[2].strip() if len(lines) > 2 else ""
    elif action == "delete":
        args["memory_id"] = lines[1].strip() if len(lines) > 1 else ""
    elif action == "search":
        args["text"] = lines[1].strip() if len(lines) > 1 else ""
    elif action == "list":
        if len(lines) > 1 and lines[1].strip():
            args["category"] = lines[1].strip().lower()
    return args


def _parse_write_file(content: str) -> Dict:
    lines = content.split("\n", 1)
    return {"path": lines[0].strip(), "content": lines[1] if len(lines) > 1 else ""}


_MCP_ARG_PARSERS: Dict[str, Callable[[str], Dict[str, str]]] = {
    "bash":           lambda c: {"command": c},
    "python":         lambda c: {"code": c},
    "web_search":     lambda c: {"query": c.split("\n")[0].strip()},
    "web_fetch":      lambda c: {"url": c.split("\n")[0].strip()},
    "read_file":      lambda c: {"path": c.split("\n")[0].strip()},
    "write_file":     _parse_write_file,
    "generate_image": _parse_generate_image,
    "manage_memory":  _parse_manage_memory,
}


def _build_mcp_args(tool: str, content: str) -> Dict:
    """Convert fenced-block text content to structured MCP arguments."""
    parser = _MCP_ARG_PARSERS.get(tool)
    return parser(content) if parser else {}


async def _call_mcp_tool(
    tool: str,
    content: str,
    progress_cb: Optional[Callable[[Dict], Awaitable[None]]] = None,
) -> Dict:
    """Route a legacy tool call through the MCP manager, with direct fallbacks."""
    mcp = get_mcp_manager()
    if not mcp:
        return await _direct_fallback(tool, content, progress_cb=progress_cb) or {"error": f"MCP manager not available for tool '{tool}'", "exit_code": 1}

    server_id, tool_name = _MCP_TOOL_MAP[tool]
    qualified = f"mcp__{server_id}__{tool_name}"
    args = _build_mcp_args(tool, content)
    result = await mcp.call_tool(qualified, args)

    # If MCP server not connected, try direct fallback
    if isinstance(result, dict) and result.get("exit_code") == 1 and "not connected" in result.get("error", ""):
        fallback = await _direct_fallback(tool, content, progress_cb=progress_cb)
        if fallback:
            return fallback

    # generate_image runs as a text-only MCP tool, so the saved image URL never
    # reaches the agent loop's structured forwarding (which renders the image via
    # buildImageBubble on result["image_url"]). Lift it out of the tool's stdout so
    # the image renders deterministically — no dependence on the model echoing the
    # URL into its prose (which it mangles/hallucinates).
    if tool == "generate_image":
        _promote_image_fields(result)

    return result


def _promote_image_fields(result: Dict) -> None:
    """Lift the image URL (+ prompt/model/size) from a successful generate_image MCP
    text result into structured fields the agent loop already forwards to
    buildImageBubble. Only acts on a dict result with exit_code 0; matches the
    generated-image URL by pattern (absolute or relative) so it's robust to the
    result's wording."""
    if not isinstance(result, dict) or result.get("exit_code") != 0:
        return
    out = result.get("stdout") or ""
    m = re.search(r'(?:https?://[^\s)\]]+)?/api/generated-image/[A-Za-z0-9._-]+', out)
    if not m:
        return
    result["image_url"] = m.group(0).strip()
    for field, pat in (
        ("image_prompt", r'^Generated image for:\s*(.+)$'),
        ("image_model", r'^model:\s*(.+)$'),
        ("image_size", r'^size:\s*(.+)$'),
    ):
        fm = re.search(pat, out, re.M)
        if fm:
            result[field] = fm.group(1).strip()


_BG_MARKERS = {"#!bg", "#bg", "# bg", "#background", "# background", "@background", "# @background"}


def _split_bg_marker(content: str):
    """If the bash content's first non-empty line is a background marker
    (e.g. `#!bg`), return (True, command_without_marker); else (False, content)."""
    lines = content.split("\n")
    i = 0
    while i < len(lines) and not lines[i].strip():
        i += 1
    if i < len(lines) and lines[i].strip().lower() in _BG_MARKERS:
        del lines[i]
        return True, "\n".join(lines).strip()
    return False, content


async def _direct_fallback(
    tool: str,
    content: str,
    progress_cb: Optional[Callable[[Dict], Awaitable[None]]] = None,
) -> Optional[Dict]:
    _subproc_env = {
        **os.environ,
        "TERM": "xterm-256color",
        "COLUMNS": "120",
        "LINES": "40",
        "HOME": _AGENT_WORKDIR,
    }

    try:
        ctx = {
            "progress_cb": progress_cb,
            "subproc_env": _subproc_env,
        }

        from src.agent_tools import TOOL_HANDLERS
        if tool in TOOL_HANDLERS:
            return await TOOL_HANDLERS[tool](content, ctx)

    except Exception as e:
        return {"error": f"{tool}: {e}", "exit_code": 1}

    return None


async def _document_tool_dispatch(
    tool: str,
    content: str,
    session_id: Optional[str] = None,
    owner: Optional[str] = None,
) -> Optional[Dict]:
    """Route a document tool through TOOL_HANDLERS with the right ctx shape."""
    from src.agent_tools import TOOL_HANDLERS
    ctx = {"session_id": session_id, "owner": owner}
    if tool in TOOL_HANDLERS:
        return await TOOL_HANDLERS[tool](content, ctx)
    return None


# ---------------------------------------------------------------------------
# Dispatcher
# ---------------------------------------------------------------------------

async def execute_tool_block(
    block: Any,
    session_id: Optional[str] = None,
    disabled_tools: Optional[set] = None,
    owner: Optional[str] = None,
    progress_cb: Optional[Callable[[Dict], Awaitable[None]]] = None,
    workspace: Optional[str] = None,
    tool_policy: Optional[Any] = None,
) -> Tuple[str, Dict]:
    """Execute a single tool block. Returns (description, result_dict).

    Thin wrapper: bind the per-turn workspace (so the path resolvers + subprocess
    cwd confine to it) for the duration of this call, then delegate. Reset on the
    way out so the binding never leaks to the next tool call.
    """
    token = _active_workspace.set(workspace or None)
    try:
        return await _execute_tool_block_impl(
            block,
            session_id=session_id,
            disabled_tools=disabled_tools,
            owner=owner,
            progress_cb=progress_cb,
            tool_policy=tool_policy,
        )
    finally:
        _active_workspace.reset(token)


async def _execute_tool_block_impl(
    block: Any,
    session_id: Optional[str] = None,
    disabled_tools: Optional[set] = None,
    owner: Optional[str] = None,
    progress_cb: Optional[Callable[[Dict], Awaitable[None]]] = None,
    tool_policy: Optional[Any] = None,
) -> Tuple[str, Dict]:
    """Execute a single tool block. Returns (description, result_dict).

    `progress_cb` is forwarded to long-running subprocess tools
    (bash, python) so the agent loop can emit `tool_progress` SSE
    events while the command is in flight. Ignored by other tools.
    """
    from src.tool_implementations import (
        do_search_chats, do_manage_tasks,
        do_manage_skills, do_api_call, do_manage_endpoints,
        do_manage_mcp, do_manage_webhooks, do_manage_tokens,
        do_manage_settings, do_manage_notes,
        do_manage_calendar,
        do_download_model, do_serve_model, do_list_served_models, do_stop_served_model,
        do_tail_serve_output,
        do_list_downloads, do_cancel_download, do_search_hf_models, do_list_cached_models,
        do_list_serve_presets, do_serve_preset, do_adopt_served_model,
        do_list_cookbook_servers,
        do_edit_image, do_trigger_research, do_manage_research, do_resolve_contact,
        do_manage_contact,
        do_vault_search, do_vault_get, do_vault_unlock,
        do_app_api,
    )

    tool = block.tool_type
    content = block.content

    # Misformatted tool call detection: model put JSON inside ```python``` (or
    # similar) without naming the tool. Common with MiniMax-style outputs.
    # Return a helpful error so the model retries with the correct format.
    if tool in ("python", "json", "xml") and content.strip().startswith("{") and content.strip().endswith("}"):
        try:
            parsed = json.loads(content.strip())
            if isinstance(parsed, dict):
                desc = f"{tool}: misformatted tool call"
                result = {
                    "error": (
                        f"You wrote a JSON object inside a ```{tool}``` block, but that's not a tool call.\n"
                        "To call a tool, use the tool name as the fence tag, e.g.\n"
                        "```resolve_contact\n"
                        "{\"name\": \"...\"}\n"
                        "```\n"
                        "or\n"
                        "```send_email\n"
                        "{\"to\": \"...\", \"subject\": \"...\", \"body\": \"...\"}\n"
                        "```"
                    ),
                    "exit_code": 1,
                }
                return desc, result
        except (ValueError, TypeError):
            pass

    # Reject tools that the user has disabled for this request
    if disabled_tools and tool in disabled_tools:
        desc = f"{tool}: BLOCKED"
        result = {"error": f"Tool '{tool}' is disabled by user.", "exit_code": 1}
        logger.info(f"Tool blocked by user: {tool}")
        return desc, result

    if tool_policy and tool_policy.blocks(tool):
        desc = f"{tool}: BLOCKED"
        result = {
            "error": f"Execution of tool '{tool}' is forbade by the active guide-only policy.",
            "exit_code": 1,
        }
        logger.warning("Tool policy blocked tool=%s", tool)
        return desc, result

    if tool in _ADMIN_TOOLS and not _owner_is_admin(owner):
        desc = f"{tool}: BLOCKED"
        result = {"error": f"Tool '{tool}' requires an admin user.", "exit_code": 1}
        logger.warning("Admin tool blocked for non-admin owner=%r tool=%s", owner, tool)
        return desc, result

    if is_public_blocked_tool(tool) and not _owner_is_admin(owner):
        desc = f"{tool}: BLOCKED"
        result = {
            "error": (
                f"Tool '{tool}' is restricted to admin users on this deployment. "
                "Ask an admin to perform this action or grant the needed permission."
            ),
            "exit_code": 1,
        }
        logger.warning("Public tool policy blocked owner=%r tool=%s", owner, tool)
        return desc, result

    # ask_user: the agent poses a multiple-choice question to the user to get a
    # decision/clarification. This is a pure UI-control marker — no subprocess,
    # no filesystem. It returns an `ask_user` payload that the agent loop turns
    # into an `ask_user` SSE event and then ENDS the turn, so the chat waits for
    # the user's selection (their choice arrives as the next message).
    if tool == "ask_user":
        question, options, multi = "", [], False
        raw = (content or "").strip()
        try:
            parsed = json.loads(raw) if raw else {}
        except (ValueError, TypeError):
            parsed = {}
        if isinstance(parsed, dict):
            question = str(parsed.get("question", "")).strip()
            multi = bool(parsed.get("multi") or parsed.get("multiSelect"))
            for opt in (parsed.get("options") or []):
                if isinstance(opt, dict):
                    label = str(opt.get("label", "")).strip()
                    descr = str(opt.get("description", "")).strip()
                elif isinstance(opt, str):
                    label, descr = opt.strip(), ""
                else:
                    continue
                if label:
                    options.append({"label": label, "description": descr})
        else:
            question = raw
        if not question or len(options) < 2:
            return "ask_user: invalid", {
                "error": (
                    "ask_user needs a non-empty `question` and at least 2 `options` "
                    "(each an object with a `label`, optional `description`)."
                ),
                "exit_code": 1,
            }
        options = options[:6]  # keep the choice list sane
        desc = f"ask_user: {question[:80]}"
        labels = ", ".join(o["label"] for o in options)
        result = {
            "ask_user": {"question": question, "options": options, "multi": multi},
            "output": f"Asked the user: {question}\nOptions: {labels}\nAwaiting their selection.",
            "exit_code": 0,
        }
        logger.info("Tool executed: %s (%d options, multi=%s)", desc, len(options), multi)
        return desc, result

    # update_plan: the agent writes back to the active plan — tick an item done
    # or revise steps (e.g. when the user asks to change something). Pure UI
    # marker: returns a `plan_update` payload the agent loop turns into a
    # `plan_update` SSE event; the frontend replaces the stored plan and refreshes
    # the docked plan window. Does NOT end the turn.
    if tool == "update_plan":
        import json as _json
        raw = (content or "").strip()
        plan = ""
        try:
            parsed = _json.loads(raw) if raw else {}
        except (ValueError, TypeError):
            parsed = {}
        if isinstance(parsed, dict) and parsed.get("plan"):
            plan = str(parsed.get("plan", "")).strip()
        else:
            # Plain-string call (raw checklist) or JSON without a usable `plan`.
            plan = raw
        if not plan:
            return "update_plan: invalid", {
                "error": "update_plan needs a non-empty `plan` (the full updated checklist as markdown).",
                "exit_code": 1,
            }
        plan = plan[:8192]
        done = plan.count("- [x]") + plan.count("- [X]")
        total = done + plan.count("- [ ]")
        desc = f"update_plan: {done}/{total} done" if total else "update_plan"
        result = {
            "plan_update": {"plan": plan},
            "output": f"Plan updated ({done}/{total} steps complete)." if total else "Plan updated.",
            "exit_code": 0,
        }
        logger.info("Tool executed: %s", desc)
        return desc, result

    # Background execution: a `bash` block whose first line is the `#!bg`
    # marker runs DETACHED — returns a job id immediately so the chat stream
    # isn't held open for a multi-minute install/ffmpeg/download. The always-on
    # monitor re-invokes the agent with the full output when the job finishes.
    if tool == "bash" and session_id:
        _is_bg, _bg_cmd = _split_bg_marker(content)
        if _is_bg and _bg_cmd:
            from src import bg_jobs
            rec = bg_jobs.launch(_bg_cmd, session_id=session_id, cwd=agent_cwd())
            short = _bg_cmd.strip().split(chr(10))[0][:80]
            desc = f"bash (background): {short}"
            result = {
                "output": (
                    f"Started background job `{rec['id']}`. It is running detached — "
                    f"do NOT wait for it or poll it. You will be automatically re-invoked "
                    f"with its full output when it finishes. Continue with other work, or "
                    f"end your turn now and resume when the result arrives."
                ),
                "exit_code": 0,
                "bg_job_id": rec["id"],
            }
            logger.info(f"Tool executed: {desc} -> bg job {rec['id']}")
            return desc, result

    # Route MCP-extracted tools through the MCP manager. Forward
    # the progress callback so long-running subprocess tools
    # (bash, python) can stream `tool_progress` events to the UI.
    if tool in _MCP_TOOL_MAP:
        first_line = content.split(chr(10))[0][:80]
        desc = f"{tool}: {first_line}"
        result = await _call_mcp_tool(tool, content, progress_cb=progress_cb)
    elif tool in ("grep", "glob", "ls", "get_workspace"):
        # Code-navigation tools — no MCP server; run the direct implementation.
        first_line = content.split(chr(10))[0][:80]
        desc = f"{tool}: {first_line}"
        result = await _direct_fallback(tool, content, progress_cb=progress_cb) \
            or {"error": f"{tool}: execution failed", "exit_code": 1}
    elif tool in ("create_document", "update_document", "edit_document",
                  "suggest_document", "manage_documents"):
        desc = f"{tool}: {content.split(chr(10))[0][:80]}"
        result = await _document_tool_dispatch(tool, content, session_id, owner) \
            or {"error": f"{tool}: execution failed", "exit_code": 1}
        if tool in ("edit_document", "suggest_document") and "title" in (result or {}):
            desc = f"{tool}: {result.get('title', '')}"
    elif tool == "search_chats":
        query = content.split("\n")[0].strip()
        desc = f"search_chats: {query[:80]}"
        result = await do_search_chats(query, owner=owner)
    elif tool in ("chat_with_model", "create_session", "list_sessions",
                  "send_to_session", "pipeline",
                  "manage_session", "manage_memory", "list_models",
                  "ui_control", "ask_teacher"):
        from src.ai_interaction import dispatch_ai_tool
        desc, result = await dispatch_ai_tool(tool, content, session_id, owner=owner)
    elif tool == "manage_tasks":
        desc = "manage_tasks"
        result = await do_manage_tasks(content, owner=owner)
    elif tool == "manage_skills":
        desc = "manage_skills"
        result = await do_manage_skills(content, owner=owner)
    elif tool == "api_call":
        first_line = content.split("\n")[0].strip()[:60]
        desc = f"api_call: {first_line}"
        result = await do_api_call(content)
    elif tool == "manage_endpoints":
        desc = "manage_endpoints"
        result = await do_manage_endpoints(content, owner=owner)
    elif tool == "manage_mcp":
        desc = "manage_mcp"
        result = await do_manage_mcp(content, owner=owner)
    elif tool == "manage_webhooks":
        desc = "manage_webhooks"
        result = await do_manage_webhooks(content, owner=owner)
    elif tool == "manage_tokens":
        desc = "manage_tokens"
        result = await do_manage_tokens(content, owner=owner)
    elif tool == "manage_settings":
        desc = "manage_settings"
        result = await do_manage_settings(content, owner=owner)
    elif tool == "manage_notes":
        desc = "manage_notes"
        result = await do_manage_notes(content, owner=owner)
    elif tool == "manage_calendar":
        desc = "manage_calendar"
        result = await do_manage_calendar(content, owner=owner)
    elif tool == "download_model":
        desc = "download_model"
        result = await do_download_model(content, owner=owner)
    elif tool == "serve_model":
        desc = "serve_model"
        result = await do_serve_model(content, owner=owner)
    elif tool == "list_served_models":
        desc = "list_served_models"
        result = await do_list_served_models(content, owner=owner)
    elif tool == "stop_served_model":
        desc = "stop_served_model"
        result = await do_stop_served_model(content, owner=owner)
    elif tool == "tail_serve_output":
        desc = "tail_serve_output"
        result = await do_tail_serve_output(content, owner=owner)
    elif tool == "list_downloads":
        desc = "list_downloads"
        result = await do_list_downloads(content, owner=owner)
    elif tool == "cancel_download":
        desc = "cancel_download"
        result = await do_cancel_download(content, owner=owner)
    elif tool == "search_hf_models":
        desc = "search_hf_models"
        result = await do_search_hf_models(content, owner=owner)
    elif tool == "list_cached_models":
        desc = "list_cached_models"
        result = await do_list_cached_models(content, owner=owner)
    elif tool == "app_api":
        desc = "app_api"
        result = await do_app_api(content, owner=owner)
    elif tool == "list_serve_presets":
        desc = "list_serve_presets"
        result = await do_list_serve_presets(content, owner=owner)
    elif tool == "serve_preset":
        desc = "serve_preset"
        result = await do_serve_preset(content, owner=owner)
    elif tool == "adopt_served_model":
        desc = "adopt_served_model"
        result = await do_adopt_served_model(content, owner=owner)
    elif tool == "list_cookbook_servers":
        desc = "list_cookbook_servers"
        result = await do_list_cookbook_servers(content, owner=owner)
    elif tool == "edit_image":
        desc = "edit_image"
        result = await do_edit_image(content, owner=owner)
    elif tool == "edit_file":
        result = await _direct_fallback(tool, content) or {"error": "edit failed", "exit_code": 1}
        desc = result.get("output") or result.get("error") or "edit_file"
    elif tool == "trigger_research":
        desc = "trigger_research"
        result = await do_trigger_research(content, owner=owner)
    elif tool == "manage_research":
        desc = "manage_research"
        result = await do_manage_research(content, owner=owner)
    elif tool == "resolve_contact":
        desc = "resolve_contact"
        result = await do_resolve_contact(content, owner=owner)
    elif tool == "manage_contact":
        desc = "manage_contact"
        result = await do_manage_contact(content, owner=owner)
    elif tool == "vault_search":
        desc = "vault_search"
        result = await do_vault_search(content, owner=owner)
    elif tool == "vault_get":
        desc = "vault_get"
        result = await do_vault_get(content, owner=owner)
    elif tool == "vault_unlock":
        desc = "vault_unlock"
        result = await do_vault_unlock(content, owner=owner)
    elif tool.startswith("mcp__"):
        # MCP tool dispatch
        mcp = get_mcp_manager()
        if mcp:
            try:
                args = json.loads(content) if content.strip().startswith("{") else {}
            except (json.JSONDecodeError, TypeError):
                args = {}
            desc = f"mcp: {tool}"
            result = await mcp.call_tool(tool, args)
        else:
            desc = f"mcp: {tool}"
            result = {"error": "MCP manager not available", "exit_code": 1}
    else:
        desc = f"unknown: {tool}"
        result = {"error": f"Unknown tool type: {tool}", "exit_code": 1}

    logger.info(f"Tool executed: {desc} -> exit_code={result.get('exit_code', 'n/a')}")
    return desc, result


# ---------------------------------------------------------------------------
# Result formatting
# ---------------------------------------------------------------------------

# Keys handled by the dedicated branches below — never echo them as raw JSON.
_FORMATTER_HANDLED_KEYS = {
    "stdout", "stderr", "exit_code", "content", "size",
    "response", "results", "session_id", "name", "model", "session_name",
    "success", "path", "action", "title", "doc_id", "version", "applied",
    "error", "output",
}


def format_tool_result(description: str, result: Dict) -> str:
    """Format a tool result into text for feeding back to the LLM."""
    parts = [f"### {description}"]

    if "stdout" in result:
        if result["stdout"]:
            parts.append(f"**stdout:**\n```\n{result['stdout']}\n```")
        if result["stderr"]:
            parts.append(f"**stderr:**\n```\n{result['stderr']}\n```")
        parts.append(f"**exit_code:** {result.get('exit_code', 'unknown')}")
    elif "output" in result:
        # bash / python canonical result shape: {"output": ..., "exit_code": ...}
        parts.append(f"```\n{result['output']}\n```")
        if result.get("exit_code") not in (0, None):
            parts.append(f"**exit_code:** {result['exit_code']}")
    elif "content" in result:
        parts.append(f"**content ({result.get('size', '?')} chars):**\n```\n{result['content']}\n```")
    elif "response" in result:
        model = result.get("model", result.get("session_name", ""))
        if model:
            parts.append(f"**{model} responded:**\n{result['response']}")
        else:
            parts.append(result["response"])
    elif "results" in result:
        parts.append(result["results"])
    elif "session_id" in result and "name" in result:
        parts.append(f"Session created: **{result['name']}** (id: `{result['session_id']}`, model: {result.get('model', 'unknown')})")
    elif "success" in result:
        if result["success"]:
            parts.append(f"File written: {result['path']} ({result['size']} bytes)")
        else:
            parts.append(f"Error: {result.get('error', 'unknown')}")
    elif "action" in result:
        action = result["action"]
        if action == "create":
            parts.append(f"Document created: \"{result.get('title', '')}\" (id: {result['doc_id']}, v{result['version']})")
        elif action == "update":
            parts.append(f"Document updated: \"{result.get('title', '')}\" (v{result['version']})")
        elif action == "edit":
            parts.append(f'Document edited: "{result.get("title", "")}" (v{result.get("version", "?")}, {result.get("applied", 0)} edit(s) applied)')
    elif "error" in result:
        parts.append(f"**Error:** {result['error']}")

    # Surface any additional structured payload (events, tasks, notes, calendars,
    # documents, attachments, etc.) that the dedicated branches above don't show.
    # Without this, tools that return {"response": "...", "events": [...]} would
    # silently drop the events list and the model would only see the summary line.
    extra = {k: v for k, v in result.items() if k not in _FORMATTER_HANDLED_KEYS}
    if extra:
        try:
            extra_json = json.dumps(extra, indent=2, default=str, ensure_ascii=False)
            # Cap to avoid blowing the context window on huge payloads.
            if len(extra_json) > 8000:
                extra_json = extra_json[:8000] + f"\n... (truncated, {len(extra_json)} chars total)"
            parts.append(f"**data:**\n```json\n{extra_json}\n```")
        except (TypeError, ValueError):
            pass

    return "\n".join(parts)