"""Base class for forensic analysis agents."""

from __future__ import annotations

import json
import logging
import time
import uuid
from typing import Any

from evidence_graph import EvidenceGraph
from llm_client import LLMClient

logger = logging.getLogger(__name__)


def _log(msg: str, **extra) -> None:
    """Emit a structured log message with extra fields."""
    logger.info(msg, extra=extra)


class BaseAgent:
    """Base class for all forensic agents.

    Each agent has:
    - A name and role description
    - A set of tools it can use (registered as methods)
    - Access to the shared EvidenceGraph
    - An LLM client for reasoning and tool-calling
    """

    name: str = "base"
    role: str = "A forensic analysis agent."

    # Tools the agent MUST invoke at least once for the run to count as productive.
    # If none of these were called when tool_call_loop returns, run() fires a
    # forced retry with an explicit "you forgot to record" instruction.
    # Subclasses override to declare their own recording responsibility
    # (timeline → add_temporal_edge, hypothesis → add_hypothesis, report → save_report).
    # observe_identity (S5) counts as a recording too — it writes through the
    # same grounding gateway and produces an identity_observation phenomenon.
    mandatory_record_tools: tuple[str, ...] = ("add_phenomenon", "observe_identity")

    # Tools whose invocation ends the run immediately. After any terminal tool
    # is called, tool_call_loop returns with that tool's result text as
    # final_text. Used by agents whose "completion" is a single explicit
    # action rather than "model decides to stop calling tools". For multi-call
    # agents (filesystem records many phenomena) leave empty.
    terminal_tools: tuple[str, ...] = ()

    def __init__(self, llm: LLMClient, graph: EvidenceGraph) -> None:
        self.llm = llm
        self.graph = graph
        self._tools: dict[str, dict] = {}  # name -> schema
        self._executors: dict[str, Any] = {}  # name -> async callable
        self._record_call_counts: dict[str, int] = {}
        self._work_log: list[str] = []
        self._current_lead_id: str | None = None

    def register_tool(
        self,
        name: str,
        description: str,
        input_schema: dict,
        executor: Any,
    ) -> None:
        """Register a tool that this agent can use."""
        self._tools[name] = {
            "name": name,
            "description": description,
            "input_schema": input_schema,
        }
        if name in self.mandatory_record_tools:
            self._executors[name] = self._wrap_record_executor(name, executor)
        else:
            self._executors[name] = executor

    def _wrap_record_executor(self, name: str, executor: Any) -> Any:
        """Wrap a mandatory-record executor to count successful invocations."""
        async def wrapped(*args, **kwargs):
            result = await executor(*args, **kwargs)
            self._record_call_counts[name] = self._record_call_counts.get(name, 0) + 1
            return result
        return wrapped

    def get_tool_definitions(self) -> list[dict]:
        """Get tool definitions in Claude API format."""
        return list(self._tools.values())

    def _build_system_prompt(self, task: str) -> str:
        """Build the system prompt — lightweight stats, no full evidence dump."""
        work_log_section = ""
        if self._work_log:
            entries = self._work_log[-5:]
            log_lines = "\n".join(f"  {i+1}. {entry}" for i, entry in enumerate(entries))
            work_log_section = (
                f"\nYour prior work on this investigation:\n{log_lines}\n"
                f"Avoid repeating tools/approaches that already succeeded or failed. Build on prior findings.\n"
            )

        return (
            f"You are {self.name}, a specialized digital forensics agent.\n"
            f"Role: {self.role}\n\n"
            f"You are analyzing a disk image as part of a multi-agent forensic investigation.\n"
            f"Image: {self.graph.image_path}\n\n"
            f"Current investigation state:\n{self.graph.stats_summary()}\n"
            f"{work_log_section}\n"
            f"Your current task: {task}\n\n"
            f"CRITICAL WORKFLOW — you MUST follow these steps IN ORDER, one phase at a time:\n\n"
            f"Phase A — INVESTIGATE:\n"
            f"  Use list_phenomena/search_graph to review existing findings.\n"
            f"  Call list_assets to see what files are already extracted.\n"
            f"  Call investigation tools (list_directory, parse_registry_key, etc.) to gather data.\n"
            f"  Only extract_file for forensically relevant files (user data, logs, configs, hives) — NOT system DLLs or OS files.\n"
            f"  Create add_lead for anything outside your expertise.\n\n"
            f"Phase B — RECORD PHENOMENA (GROUNDED):\n"
            f"  For EACH significant finding from Phase A, call add_phenomenon with:\n"
            f"    * interpretation: your analysis — free text, NOT verified.\n"
            f"    * verified_facts: one entry per concrete atom (path, timestamp,\n"
            f"      inode, hash, identifier, count) you want recorded as truth.\n"
            f"      Each entry MUST have:\n"
            f"        - type: e.g. 'path', 'timestamp', 'inode', 'hash', 'identifier', 'count'\n"
            f"        - value: a VERBATIM substring from the tool output\n"
            f"        - invocation_id: the inv-xxx ID from the '[invocation: inv-xxx]'\n"
            f"          header at the top of the tool result that produced this value\n"
            f"  IDENTIFIERS — call observe_identity (in ADDITION to add_phenomenon)\n"
            f"  whenever you see an email, phone number, Apple ID, IMEI, wallet\n"
            f"  address, MAC, UDID, persistent nickname, or display name. Same\n"
            f"  grounding contract: value must be verbatim in the cited tool\n"
            f"  output. This is HOW cross-source attribution gets built — without\n"
            f"  it, we can't tell whether the Apple ID in keychain belongs to the\n"
            f"  same person as the Windows account on the USB.\n"
            f"  Do NOT call link_to_entity yet — just record all phenomena first.\n\n"
            f"Phase C — LINK ENTITIES:\n"
            f"  FIRST call list_phenomena to get the current IDs — do NOT rely on memory.\n"
            f"  Then call link_to_entity for each relevant phenomenon.\n"
            f"  NEVER guess or fabricate a phenomenon ID. If an ID is not in list_phenomena output, it does not exist.\n\n"
            f"Phase D — STOP:\n"
            f"  Once all phenomena are recorded and entities linked, you are DONE.\n"
            f"  Do not call any more tools. The orchestrator picks up automatically.\n\n"
            f"CRITICAL — RECORDING REQUIREMENT:\n"
            f"- Only graph mutations propagate to other agents and the final report.\n"
            f"- You MUST call add_phenomenon for EVERY significant finding BEFORE you stop.\n"
            f"- NEGATIVE findings count too. If you searched X (a directory, a pattern, "
            f"a registry key) and found NOTHING, that absence IS evidence — call "
            f"add_phenomenon with a 'No matches for X' title, the search scope in "
            f"raw_data, and cite the search tool's invocation_id (verified_facts may "
            f"be empty for a true negative; the cited invocation in source_tool still "
            f"anchors it). Negative findings constrain the hypothesis space.\n"
            f"- If you stop without having called add_phenomenon at least once, the task "
            f"is FAILED and a forced retry will fire.\n\n"
            f"GROUNDING GATEWAY — STRUCTURALLY ENFORCED:\n"
            f"- Every tool result begins with '[invocation: inv-xxxxxxxx]' — that ID\n"
            f"  is what you cite in each fact's invocation_id.\n"
            f"- fact.value must be a substring of the cited invocation's output.\n"
            f"  Case, whitespace, and path-separator (/ ↔ \\) variants are tolerated;\n"
            f"  anything else fabricated is REJECTED with a per-fact reason.\n"
            f"- On REJECTED: quote the literal text from the output (or drop the\n"
            f"  fact), and put guesses / inferred paths / model names in\n"
            f"  `interpretation` instead. Then call add_phenomenon again.\n"
            f"- You may cite ONLY invocations made within THIS task."
        )

    async def run(self, task: str, lead_id: str | None = None) -> str:
        """Run this agent with a specific task."""
        _log(task, event="agent_start", agent=self.name)
        self.graph.agent_status[self.name] = "running"
        self.graph._current_agent = self.name
        # Fresh task scope per agent run. Used by the grounding gateway to
        # check that facts in add_phenomenon cite invocations made *within
        # this run* — preventing the agent from forwarding stale IDs from
        # earlier work or another agent.
        self.graph._current_task_id = f"task-{uuid.uuid4().hex[:8]}"
        self._current_lead_id = lead_id

        self._register_graph_tools()
        self._record_call_counts.clear()

        system = self._build_system_prompt(task)
        messages = [{"role": "user", "content": task}]

        t0 = time.monotonic()
        ph_before = len(self.graph.phenomena)

        try:
            final_text, conversation = await self.llm.tool_call_loop(
                messages=messages,
                tools=self.get_tool_definitions(),
                tool_executor=self._executors,
                system=system,
                terminal_tools=self.terminal_tools,
            )

            # Forced-record retry: if the agent has any mandatory recording
            # tools but never invoked any of them, force one more round with
            # an explicit "you forgot to record" instruction. The mandatory
            # set is declared on the class — Timeline → add_temporal_edge,
            # Hypothesis → add_hypothesis, ReportAgent → (). For agents with
            # empty mandatory_record_tools this branch is a no-op.
            registered_mandatory = [
                t for t in self.mandatory_record_tools if t in self._executors
            ]
            recorded_any = any(
                self._record_call_counts.get(t, 0) > 0
                for t in registered_mandatory
            )
            if registered_mandatory and not recorded_any:
                missing = "/".join(registered_mandatory)
                logger.warning(
                    "[%s] finished without calling any of [%s] — forcing RECORD retry",
                    self.name, missing,
                )
                conversation.append({
                    "role": "user",
                    "content": (
                        f"STOP. You produced an answer without ever calling "
                        f"{missing}. Your answer is DISCARDED — only graph "
                        f"mutations propagate to other agents and the final "
                        f"report.\n\n"
                        f"You MUST now call {missing} for every significant "
                        f"finding from your prior investigation, including "
                        f"exact identifiers, timestamps, and the source_tool "
                        f"that produced each finding. If you genuinely found "
                        f"NOTHING noteworthy, call the recording tool ONCE "
                        f"with a 'No significant findings' style entry "
                        f"summarizing what you searched.\n\n"
                        f"Do not run more investigation tools. Just record "
                        f"what you already found. Then end."
                    ),
                })
                final_text, _ = await self.llm.tool_call_loop(
                    messages=conversation,
                    tools=self.get_tool_definitions(),
                    tool_executor=self._executors,
                    system=system,
                    max_iterations=10,
                    terminal_tools=self.terminal_tools,
                )

            self._work_log.append(f"[Task: {task[:80]}] -> {final_text[:150]}")
        except Exception:
            self.graph.agent_status[self.name] = "failed"
            logger.error("[%s] Failed during task execution", self.name, exc_info=True)
            raise

        self.graph.agent_status[self.name] = "completed"
        elapsed = time.monotonic() - t0
        new_ph = len(self.graph.phenomena) - ph_before
        _log(f"+{new_ph} phenomena, {len(final_text)} chars", event="agent_done", agent=self.name, elapsed=elapsed)
        return final_text

    # ---- Graph interaction tools --------------------------------------------

    def _register_graph_tools(self) -> None:
        """Register graph query + mutation tools.

        Subclasses can override to restrict the toolset. For example, a
        read-only agent (hypothesis, report) overrides this to skip
        _register_graph_write_tools.
        """
        self._register_graph_read_tools()
        self._register_graph_write_tools()

    def _register_graph_read_tools(self) -> None:
        """Register read-only graph + asset query tools."""

        self.register_tool(
            name="list_phenomena",
            description=(
                "List all phenomena (evidence artifacts) on the graph. "
                "Returns one-line summaries with IDs. Use get_phenomenon(id) for full details."
            ),
            input_schema={
                "type": "object",
                "properties": {
                    "category": {
                        "type": "string",
                        "description": "Filter by category (filesystem, registry, email, network, timeline). Omit for all.",
                    },
                },
            },
            executor=self._list_phenomena,
        )

        self.register_tool(
            name="get_phenomenon",
            description="Get full details of a specific phenomenon by ID, including raw_data.",
            input_schema={
                "type": "object",
                "properties": {
                    "id": {"type": "string", "description": "Phenomenon ID (e.g. 'ph-a1b2c3d4')."},
                },
                "required": ["id"],
            },
            executor=self._get_phenomenon,
        )

        self.register_tool(
            name="search_graph",
            description="Search across phenomena, hypotheses, and entities by keyword. Returns matching summaries.",
            input_schema={
                "type": "object",
                "properties": {
                    "keyword": {"type": "string", "description": "Search keyword."},
                },
                "required": ["keyword"],
            },
            executor=self._search_graph,
        )

        self.register_tool(
            name="get_related",
            description="Get all nodes connected to a given node via edges. Returns summaries and edge types.",
            input_schema={
                "type": "object",
                "properties": {
                    "node_id": {"type": "string", "description": "Any node ID (ph-*, hyp-*, ent-*)."},
                },
                "required": ["node_id"],
            },
            executor=self._get_related,
        )

        self.register_tool(
            name="get_hypothesis_status",
            description="Get current status and confidence of all hypotheses being investigated.",
            input_schema={"type": "object", "properties": {}},
            executor=self._get_hypothesis_status,
        )

        self.register_tool(
            name="list_assets",
            description=(
                "List all files extracted from the disk image. "
                "Shows filename, category, size, local path, and inode. "
                "Check this before calling extract_file to avoid re-extraction."
            ),
            input_schema={
                "type": "object",
                "properties": {
                    "category": {
                        "type": "string",
                        "enum": [
                            "registry_hive", "chat_log", "prefetch", "network_capture",
                            "config_file", "address_book", "recycle_bin", "executable",
                            "text_log", "other",
                        ],
                        "description": "Filter by category. Omit to list all.",
                    },
                },
            },
            executor=self._list_assets,
        )

        self.register_tool(
            name="find_extracted_file",
            description=(
                "Find an already-extracted file by inode or filename. "
                "Returns the local path so you can use it directly with "
                "parse_registry_key, read_text_file, etc. without re-extracting."
            ),
            input_schema={
                "type": "object",
                "properties": {
                    "inode": {"type": "string", "description": "Inode to look up."},
                    "filename": {"type": "string", "description": "Filename or partial name to search."},
                },
            },
            executor=self._find_extracted_file,
        )

    def _register_graph_write_tools(self) -> None:
        """Register graph mutation tools (add_phenomenon, add_lead, link_to_entity)."""

        self.register_tool(
            name="add_phenomenon",
            description=(
                "Record a forensic finding on the evidence graph. The finding is "
                "split into provenance-bound atoms (verified_facts) and free-form "
                "analysis (interpretation). Each fact MUST cite the invocation_id "
                "of a tool call you made in THIS task — the gateway checks every "
                "fact's value against that call's real output, byte-for-byte. "
                "Any fact that fails grounding causes the whole record to be "
                "rejected with a list of failures; fix the facts and call again."
            ),
            input_schema={
                "type": "object",
                "properties": {
                    "category": {"type": "string", "description": "Category of the finding."},
                    "title": {"type": "string", "description": "Short title."},
                    "interpretation": {
                        "type": "string",
                        "description": (
                            "Free-form analysis text — your reasoning, why this "
                            "matters, what it implies. NOT verified by the gateway. "
                            "Rendered in reports as 'agent analysis', not truth."
                        ),
                    },
                    "verified_facts": {
                        "type": "array",
                        "description": (
                            "Atoms you want preserved as ground truth. Each must "
                            "appear verbatim in the cited tool output."
                        ),
                        "items": {
                            "type": "object",
                            "properties": {
                                "type": {
                                    "type": "string",
                                    "description": (
                                        "Kind of fact: path, timestamp, inode, "
                                        "hash, identifier, count, raw, ..."
                                    ),
                                },
                                "value": {
                                    "type": "string",
                                    "description": (
                                        "Verbatim substring from the cited tool "
                                        "output. The gateway does a literal "
                                        "string-in-string check — no paraphrasing."
                                    ),
                                },
                                "invocation_id": {
                                    "type": "string",
                                    "description": (
                                        "ID from the '[invocation: inv-xxx]' header "
                                        "of the tool call that produced this value."
                                    ),
                                },
                            },
                            "required": ["type", "value", "invocation_id"],
                        },
                    },
                    "raw_data": {"type": "object", "description": "Structured raw data supporting this finding."},
                    "timestamp": {"type": "string", "description": "Timestamp if any. ONLY use timestamps from tool output."},
                    "source_tool": {"type": "string", "description": "Name of the tool that produced this (e.g. 'list_directory')."},
                },
                "required": ["category", "title", "source_tool"],
            },
            executor=self._add_phenomenon,
        )

        self.register_tool(
            name="add_lead",
            description="Create an investigative lead for another agent to follow up on.",
            input_schema={
                "type": "object",
                "properties": {
                    "target_agent": {
                        "type": "string",
                        "enum": ["filesystem", "registry", "communication", "network", "timeline"],
                        "description": "Which agent should handle this lead.",
                    },
                    "description": {"type": "string", "description": "What should be investigated."},
                    "priority": {"type": "integer", "description": "Priority 1 (highest) to 10 (lowest). Default 5."},
                },
                "required": ["target_agent", "description"],
            },
            executor=self._add_lead,
        )

        self.register_tool(
            name="link_to_entity",
            description=(
                "Link a phenomenon to a named entity (person, program, host, etc). "
                "Creates the entity if it doesn't exist."
            ),
            input_schema={
                "type": "object",
                "properties": {
                    "phenomenon_id": {"type": "string", "description": "Phenomenon ID to link from."},
                    "entity_name": {"type": "string", "description": "Name of the entity (e.g. 'Mr. Evil', 'mIRC.exe')."},
                    "entity_type": {
                        "type": "string",
                        "enum": ["person", "program", "file", "host", "ip_address"],
                        "description": "Type of entity.",
                    },
                    "edge_type": {
                        "type": "string",
                        "enum": ["created_by", "executed_by", "owned_by", "targets", "associated_with", "found_on", "used_by"],
                        "description": "Relationship type.",
                    },
                },
                "required": ["phenomenon_id", "entity_name", "entity_type", "edge_type"],
            },
            executor=self._link_to_entity,
        )

        self.register_tool(
            name="observe_identity",
            description=(
                "Record a typed identifier (email / phone / Apple ID / IMEI / "
                "wallet address / nickname / display name / …) for an entity. "
                "Goes through the same grounding gateway as add_phenomenon — "
                "value MUST be a verbatim substring of the cited tool output. "
                "After attachment, the engine automatically proposes / "
                "strengthens / weakens cross-source coreference hypotheses "
                "between this entity and any others carrying the same or "
                "conflicting identifiers. This is how 'is the Apple ID in iOS "
                "keychain the same person as the Windows login name?' gets "
                "answered. Call this in ADDITION to add_phenomenon for "
                "identifier-bearing findings."
            ),
            input_schema={
                "type": "object",
                "properties": {
                    "entity_name": {"type": "string", "description": "Human-readable entity name (e.g. 'LEUNG YL', 'alice@example.com')."},
                    "entity_type": {
                        "type": "string",
                        "enum": ["person", "program", "file", "host", "ip_address"],
                        "description": "Kind of entity this identifier belongs to (usually 'person').",
                    },
                    "identifier_type": {
                        "type": "string",
                        "description": (
                            "Strong (near-unique): email, phone_number, imei, "
                            "imsi, apple_id, icloud_id, google_account, "
                            "wallet_address, udid, mac_address, device_serial. "
                            "Weak (free-form, may collide): nickname, "
                            "display_name, username, screen_name."
                        ),
                    },
                    "value": {
                        "type": "string",
                        "description": (
                            "The identifier value, quoted VERBATIM from the "
                            "tool output you cite in invocation_id."
                        ),
                    },
                    "invocation_id": {
                        "type": "string",
                        "description": (
                            "ID from the '[invocation: inv-xxx]' header of "
                            "the tool call that surfaced this identifier."
                        ),
                    },
                    "source_tool": {
                        "type": "string",
                        "description": "Name of the tool that produced the identifier.",
                    },
                },
                "required": [
                    "entity_name", "entity_type", "identifier_type",
                    "value", "invocation_id",
                ],
            },
            executor=self._observe_identity,
        )

    # ---- Tool executors -----------------------------------------------------

    async def _list_phenomena(self, category: str | None = None) -> str:
        results = self.graph.list_phenomena(category)
        if not results:
            return "No phenomena recorded yet." if not category else f"No phenomena in category '{category}'."
        return "\n".join(results)

    async def _get_phenomenon(self, id: str) -> str:
        data = self.graph.get_phenomenon(id)
        if data is None:
            return f"Phenomenon not found: {id}"
        return json.dumps(data, ensure_ascii=False, indent=2)

    async def _search_graph(self, keyword: str) -> str:
        results = self.graph.search_graph(keyword)
        if not results:
            return f"No matches for '{keyword}'."
        return "\n".join(results)

    async def _get_related(self, node_id: str) -> str:
        results = self.graph.get_related(node_id)
        if not results:
            return f"No connections found for {node_id}."
        lines = []
        for r in results:
            lines.append(f"  {r['direction']} [{r['edge_type']}] → {r['node']}")
        return "\n".join(lines)

    async def _get_hypothesis_status(self) -> str:
        results = self.graph.get_hypothesis_status()
        if not results:
            return "No hypotheses defined yet."
        return "\n".join(results)

    async def _add_phenomenon(
        self,
        category: str,
        title: str,
        interpretation: str = "",
        verified_facts: list[dict] | None = None,
        raw_data: dict | None = None,
        timestamp: str | None = None,
        source_tool: str = "",
        # Back-compat: older prompts (and accidental LLM emissions) may pass
        # ``description``; treat it as ``interpretation`` rather than failing.
        description: str | None = None,
    ) -> str:
        if description and not interpretation:
            interpretation = description
        # GroundingError propagates: llm_client._execute_single_tool turns
        # raised exceptions into "Error executing add_phenomenon: <msg>" tool
        # results the LLM sees, and _wrap_record_executor does NOT increment
        # the mandatory-record counter (the increment only runs after a
        # successful return), so the forced-retry mechanism still fires if
        # the agent never lands a grounded phenomenon.
        pid, merged = await self.graph.add_phenomenon(
            source_agent=self.name,
            category=category,
            title=title,
            interpretation=interpretation,
            verified_facts=verified_facts,
            raw_data=raw_data,
            timestamp=timestamp,
            source_tool=source_tool,
            from_lead_id=self._current_lead_id,
        )
        if merged:
            return f"Phenomenon merged into existing: {pid} — {title} (corroboration boost)"
        return f"Phenomenon recorded: {pid} — {title}"

    async def _add_lead(
        self,
        target_agent: str,
        description: str,
        priority: int = 5,
    ) -> str:
        lid = await self.graph.add_lead(
            target_agent=target_agent,
            description=description,
            priority=priority,
        )
        return f"Lead created: {lid} — [{target_agent}] {description}"

    async def _link_to_entity(
        self,
        phenomenon_id: str,
        entity_name: str,
        entity_type: str,
        edge_type: str,
    ) -> str:
        # Validate phenomenon exists before creating entity
        if not self.graph._node_exists(phenomenon_id):
            return (
                f"Error: phenomenon '{phenomenon_id}' not found. "
                f"Call list_phenomena first to get valid IDs."
            )
        eid, existing = await self.graph.add_entity(entity_name, entity_type)
        await self.graph.add_edge(
            source_id=phenomenon_id,
            target_id=eid,
            edge_type=edge_type,
            created_by=self.name,
        )
        status = "linked to existing" if existing else "created and linked"
        return f"Entity {status}: {entity_name} ({entity_type}) ←[{edge_type}]— {phenomenon_id}"

    async def _observe_identity(
        self,
        entity_name: str,
        entity_type: str,
        identifier_type: str,
        value: str,
        invocation_id: str,
        source_tool: str = "",
    ) -> str:
        # GroundingError / ValueError propagate to llm_client's per-tool
        # exception handler, which formats them back to the LLM. That keeps
        # the mandatory-record counter honest — only a successful return
        # triggers the increment in _wrap_record_executor.
        result = await self.graph.observe_identity(
            entity_name=entity_name,
            entity_type=entity_type,
            identifier_type=identifier_type,
            value=value,
            source_agent=self.name,
            source_tool=source_tool,
            invocation_id=invocation_id,
        )
        lines = [
            f"Identity observed: {identifier_type}={value} "
            f"on entity {result['entity_id']} ({entity_name})."
        ]
        if result.get("new_identifier"):
            lines.append(
                f"  Observation phenomenon: {result['phenomenon_id']}"
            )
        else:
            lines.append("  (identifier already recorded on this entity — idempotent)")
        for prop in result.get("coref_proposals", []):
            lines.append(
                f"  → Coref candidate: {prop['other_entity_id']} via "
                f"{prop['match']['edge_type']} (conf={prop['confidence']:.2f}, "
                f"hypothesis={prop['hypothesis_id']})"
            )
            for c in prop.get("conflicts", []):
                lines.append(
                    f"      ⚠ conflict on {c['type']}: "
                    f"{c['new_value']} vs {c['other_value']}"
                )
        return "\n".join(lines)

    async def _list_assets(self, category: str | None = None) -> str:
        results = self.graph.list_assets(category)
        if not results:
            return "No files extracted yet." if not category else f"No assets in category '{category}'."
        return "\n".join(results)

    async def _find_extracted_file(
        self,
        inode: str | None = None,
        filename: str | None = None,
    ) -> str:
        if inode:
            asset = self.graph.lookup_asset_by_inode(inode)
            if asset:
                return (
                    f"Found: {asset.local_path} "
                    f"({asset.size_bytes} bytes, {asset.category}, inode:{asset.inode})"
                )
            return f"No extracted file with inode {inode}."

        if filename:
            results = self.graph.query_assets(filename_pattern=filename)
            if not results:
                return f"No extracted files matching '{filename}'."
            lines = [f"Found {len(results)} matching file(s):"]
            for a in results:
                lines.append(f"  {a.local_path} (inode:{a.inode}, {a.size_bytes} bytes, {a.category})")
            return "\n".join(lines)

        return "Provide either inode or filename to search."