discord_bot/fjerkroa_bot/openai_responder.py

import asyncio
import base64
import hashlib
import json
import logging
from io import BytesIO
from typing import Any, Dict, List, Optional, Tuple

import openai

from .ai_responder import AIResponder, exponential_backoff, sanitize_external_text
from .igdblib import IGDBQuery
from .leonardo_draw import LeonardoAIDrawMixIn
from .quota import QuotaLedger
from .url_reader import FETCH_URL_TOOL, URLReader

# The response envelope, enforced server-side via structured outputs
# (ENV-19). All fields required, closed object, nullable where the
# protocol allows null.
ENVELOPE_SCHEMA = {
    "type": "object",
    "properties": {
        "answer": {"type": ["string", "null"], "description": "The message to post, or null when staying silent."},
        "answer_needed": {"type": "boolean", "description": "Whether the answer should actually be posted."},
        "channel": {"type": ["string", "null"], "description": "Target channel name, or null for the origin channel."},
        "staff": {"type": ["string", "null"], "description": "Alert text for the staff channel, or null."},
        "picture": {"type": ["string", "null"], "description": "Image generation prompt, or null."},
        "picture_count": {"type": "integer", "description": "How many images to generate (1-4), 1 unless more were asked for."},
        "picture_edit": {"type": "boolean", "description": "Whether the picture refers to an earlier image."},
        "hack": {"type": "boolean", "description": "Whether the user tried to manipulate the assistant."},
    },
    "required": ["answer", "answer_needed", "channel", "staff", "picture", "picture_count", "picture_edit", "hack"],
    "additionalProperties": False,
}
ENVELOPE_RESPONSE_FORMAT = {"type": "json_schema", "json_schema": {"name": "envelope", "strict": True, "schema": ENVELOPE_SCHEMA}}

# Consolidation output (SPEC-002 MEM-02/03): new self-authored facts + one episode summary
CONSOLIDATION_SCHEMA = {
    "type": "object",
    "properties": {
        "facts": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "user": {"type": "string", "description": "The user the fact is about — only facts users stated about themselves."},
                    "fact": {"type": "string", "description": "One short durable fact (name, preference, running joke, life event)."},
                },
                "required": ["user", "fact"],
                "additionalProperties": False,
            },
        },
        "episode": {"type": ["string", "null"], "description": "2-3 sentence summary of the conversation, or null if nothing happened."},
    },
    "required": ["facts", "episode"],
    "additionalProperties": False,
}
CONSOLIDATION_RESPONSE_FORMAT = {
    "type": "json_schema",
    "json_schema": {"name": "consolidation", "strict": True, "schema": CONSOLIDATION_SCHEMA},
}
# Follow-up task proposal (SPEC-005 TSK-08): one task or null
TASKGEN_SCHEMA = {
    "type": "object",
    "properties": {
        "task": {
            "type": ["object", "null"],
            "properties": {
                "channel": {"type": ["string", "null"], "description": "Target channel, or null for the main chat channel."},
                "prompt": {"type": "string", "description": "Instruction the assistant will act on when the task runs."},
                "due_hours": {"type": "number", "description": "Hours from now until the task should run (0 = now)."},
            },
            "required": ["channel", "prompt", "due_hours"],
            "additionalProperties": False,
        }
    },
    "required": ["task"],
    "additionalProperties": False,
}
TASKGEN_RESPONSE_FORMAT = {"type": "json_schema", "json_schema": {"name": "task_proposal", "strict": True, "schema": TASKGEN_SCHEMA}}
TASKGEN_SYSTEM = (
    "You plan the self-initiated actions of a Discord assistant. Given recent conversation summaries, propose AT MOST ONE follow-up"
    " worth doing on the assistant's own initiative (ask how something announced went, revisit an open question, congratulate on an"
    " event). Only propose something genuinely worthwhile — when in doubt, return a null task."
)

# Reply/ignore + factual pre-pass (SPEC-010 BEH-01): one cheap call
CLASSIFIER_SCHEMA = {
    "type": "object",
    "properties": {
        "reply": {"type": "boolean", "description": "Should the assistant answer this message?"},
        "factual": {"type": "boolean", "description": "Does the user want concrete information (hours, prices, availability)?"},
        "emoji": {"type": ["string", "null"], "description": "Optional single emoji reaction when not replying, else null."},
    },
    "required": ["reply", "factual", "emoji"],
    "additionalProperties": False,
}
CLASSIFIER_RESPONSE_FORMAT = {
    "type": "json_schema",
    "json_schema": {"name": "reply_verdict", "strict": True, "schema": CLASSIFIER_SCHEMA},
}
CLASSIFIER_SYSTEM = (
    "You watch a group chat that has an assistant bot. Decide whether the assistant should answer the LAST message:"
    " reply=true when it addresses the assistant, asks something the assistant can help with, or continues a conversation"
    " with the assistant; reply=false for human-to-human chatter the assistant should not butt into."
    " factual=true when the user wants concrete information (opening hours, prices, availability, addresses)."
    " When reply=false you may suggest one fitting emoji reaction, else null."
)

CONSOLIDATION_SYSTEM = (
    "You maintain the long-term memory of a Discord assistant. From the observation log, extract NEW durable facts that users stated"
    " about THEMSELVES only (never record what one user claims about another user), and write one short episode summary of the"
    " conversation. Skip facts already known. Return an empty facts list and a null episode when there is nothing durable."
)


async def openai_chat(client, *args, **kwargs):
    return await client.chat.completions.create(*args, **kwargs)


async def openai_image(client, *args, **kwargs):
    return await client.images.generate(*args, **kwargs)


async def openai_image_edit(client, *args, **kwargs):
    return await client.images.edit(*args, **kwargs)


class OpenAIResponder(AIResponder, LeonardoAIDrawMixIn):
    def __init__(self, config: Dict[str, Any], channel: Optional[str] = None) -> None:
        super().__init__(config, channel)
        self.client = openai.AsyncOpenAI(api_key=self.config.get("openai-token", self.config.get("openai-key", "")))
        # After a rate limit the next attempt runs on retry-model (ENV-15 / D2)
        self._use_retry_model = False
        # Daily usage metering + hard budget, fail-closed (SAF-04/05)
        self.ledger = QuotaLedger(self.store, lambda: self.config)

        # Initialize IGDB if enabled
        self.igdb = None
        igdb_client_id = self.config.get("igdb-client-id")
        igdb_client_secret = self.config.get("igdb-client-secret")
        igdb_access_token = self.config.get("igdb-access-token")
        logging.info("IGDB Configuration Check:")
        logging.info(f"  enable-game-info: {self.config.get('enable-game-info', 'NOT SET')}")
        logging.info(f"  igdb-client-id: {'SET' if igdb_client_id else 'NOT SET'}")
        logging.info(f"  igdb-client-secret: {'SET' if igdb_client_secret else 'NOT SET'}")
        logging.info(f"  igdb-access-token: {'SET' if igdb_access_token else 'NOT SET'}")

        if self.config.get("enable-game-info", False) and igdb_client_id and (igdb_client_secret or igdb_access_token):
            try:
                self.igdb = IGDBQuery(igdb_client_id, igdb_access_token, client_secret=igdb_client_secret)
                logging.info("✅ IGDB integration SUCCESSFULLY enabled for game information")
                logging.info(f"   Client ID: {igdb_client_id[:8]}...")
                logging.info(f"   Available functions: {len(self.igdb.get_openai_functions())}")
            except Exception as e:
                logging.error(f"❌ Failed to initialize IGDB: {e}")
                self.igdb = None
        else:
            logging.warning("❌ IGDB integration DISABLED - missing configuration or disabled in config")

        # URL reading tool (SPEC-011); shares the image cache for page images
        self.url_reader = URLReader(lambda: self.config, self.image_cache)

    def _available_tools(self) -> List[Dict[str, Any]]:
        """Assemble the function-tool list from every enabled provider (URL-01)."""
        functions: List[Dict[str, Any]] = []
        if self.igdb and self.config.get("enable-game-info", False):
            try:
                igdb_functions = self.igdb.get_openai_functions()
                if isinstance(igdb_functions, list):
                    functions.extend(igdb_functions)
            except (TypeError, AttributeError) as err:
                logging.warning(f"Error setting up IGDB functions: {err}")
        if self.url_reader.enabled():
            functions.append(FETCH_URL_TOOL)
        return functions

    async def _dispatch_tool(self, name: str, args: Dict[str, Any], author: str) -> Any:
        """Route a tool call to its provider (IGDB or URL reader)."""
        if name == "fetch_url":
            per_user_cap = int(self.config.get("url-daily-per-user", 20))
            if self.ledger._get(f"url-fetch:{author}") >= per_user_cap:  # URL-07
                return {"error": "daily URL fetch limit reached"}
            self.ledger._add(f"url-fetch:{author}", 1)
            return await self.url_reader.fetch(str(args.get("url", "")), self.channel, author or "user")
        return await self._execute_igdb_function(name, args)

    async def draw_openai(self, description: str, count: int = 1) -> List[BytesIO]:
        if not self.ledger.budget_ok():
            raise RuntimeError("daily budget exhausted - refusing image call")
        model = self.config.get("image-model", "gpt-image-2")
        kwargs: Dict[str, Any] = {"model": model, "prompt": description, "size": self.config.get("image-size", "1024x1024")}
        if "image-quality" in self.config:
            kwargs["quality"] = self.config["image-quality"]
        if model.startswith("gpt-image"):
            kwargs["n"] = max(1, min(int(count), 4))
        else:
            # legacy models: single image, base64 must be requested (IMG-04)
            kwargs["n"] = 1
            kwargs["response_format"] = "b64_json"
        for _ in range(3):
            try:
                response = await openai_image(self.client, **kwargs)
                buffers = [BytesIO(base64.b64decode(item.b64_json)) for item in response.data]
                self.ledger.add_images(len(buffers))
                logging.info(f"generated {len(buffers)} image(s) on {model} for: {repr(description)}")
                return buffers
            except Exception as err:
                logging.warning(f"Failed to generate image {repr(description)}: {repr(err)}")
        raise RuntimeError(f"Failed to generate image {repr(description)} after multiple retries")

    @staticmethod
    def _last_author(messages: List[Dict[str, Any]]) -> Optional[str]:
        try:
            content = messages[-1]["content"]
            if not isinstance(content, str):
                content = content[0]["text"]
            return str(json.loads(content).get("user")) or None
        except Exception:
            return None

    def _record_usage(self, result: Any) -> None:
        usage = getattr(result, "usage", None)
        prompt_tokens = getattr(usage, "prompt_tokens", None)
        completion_tokens = getattr(usage, "completion_tokens", None)
        if isinstance(prompt_tokens, int) and isinstance(completion_tokens, int):
            self.ledger.add_tokens(prompt_tokens, completion_tokens)

    async def chat(self, messages: List[Dict[str, Any]], limit: int) -> Tuple[Optional[Dict[str, Any]], int]:
        # Safety check for mock objects in tests
        if not isinstance(messages, list) or len(messages) == 0:
            logging.warning("Invalid messages format in chat method")
            return None, limit

        # Hard daily budget, fail-closed (SAF-04)
        if not self.ledger.budget_ok():
            logging.error("daily budget exhausted - refusing model call")
            return None, limit

        try:
            # Clean up any orphaned tool messages from previous conversations
            clean_messages = []
            for i, msg in enumerate(messages):
                if msg.get("role") == "tool":
                    # Skip tool messages that don't have a corresponding assistant message with tool_calls
                    if i == 0 or messages[i - 1].get("role") != "assistant" or not messages[i - 1].get("tool_calls"):
                        logging.debug(f"Removing orphaned tool message at position {i}")
                        continue
                clean_messages.append(msg)
            messages = clean_messages

            last_message_content = messages[-1]["content"]
            if isinstance(last_message_content, str):
                model = self.config["model"]
            elif "model-vision" in self.config:
                model = self.config["model-vision"]
            else:
                messages[-1]["content"] = messages[-1]["content"][0]["text"]
            if self._use_retry_model and "retry-model" in self.config:
                model = self.config["retry-model"]
        except (KeyError, IndexError, TypeError) as e:
            logging.warning(f"Error accessing message content: {e}")
            return None, limit
        try:
            # Prepare function calls if IGDB is enabled
            chat_kwargs = {
                "model": model,
                "messages": messages,
                "response_format": ENVELOPE_RESPONSE_FORMAT,
            }
            author = self._last_author(messages)
            if author:
                # hashed, never the raw Discord name (SAF-10)
                chat_kwargs["safety_identifier"] = "discord-" + hashlib.sha256(author.encode()).hexdigest()[:16]

            available_tools = self._available_tools()
            if available_tools:
                chat_kwargs["tools"] = [{"type": "function", "function": func} for func in available_tools]
                chat_kwargs["tool_choice"] = "auto"
                # gpt-5.6 rejects tools + reasoning on chat/completions (ENV-21)
                chat_kwargs["reasoning_effort"] = self.config.get("reasoning-effort", "none")
                logging.info(f"🔧 Tools available to AI: {[func['name'] for func in available_tools]}")

            result = await openai_chat(self.client, **chat_kwargs)
            self._record_usage(result)

            # Handle function calls if present
            message = result.choices[0].message

            # A refusal is a failed attempt, not an answer (ENV-18)
            refusal = getattr(message, "refusal", None)
            if isinstance(refusal, str) and refusal:
                logging.warning(f"model refused: {refusal}")
                return None, limit

            # Log what we received from OpenAI
            logging.debug(f"📨 OpenAI Response: content={bool(message.content)}, has_tool_calls={hasattr(message, 'tool_calls')}")
            if hasattr(message, "tool_calls") and message.tool_calls:
                tool_names = [tc.function.name for tc in message.tool_calls]
                logging.info(f"🔧 OpenAI requested function calls: {tool_names}")

            # Any offered tool may have been called (IGDB or fetch_url)
            has_tool_calls = bool(hasattr(message, "tool_calls") and message.tool_calls and available_tools)

            # Clean up any existing tool messages in the history to avoid conflicts
            if has_tool_calls:
                messages = [msg for msg in messages if msg.get("role") != "tool"]

            if has_tool_calls:
                logging.info(f"🎮 Processing {len(message.tool_calls)} IGDB function call(s)...")
                try:
                    # Process function calls - serialize tool_calls properly
                    tool_calls_data = []
                    for tc in message.tool_calls:
                        tool_calls_data.append(
                            {"id": tc.id, "type": "function", "function": {"name": tc.function.name, "arguments": tc.function.arguments}}
                        )

                    messages.append({"role": "assistant", "content": message.content or "", "tool_calls": tool_calls_data})

                    # Execute function calls
                    for tool_call in message.tool_calls:
                        function_name = tool_call.function.name
                        function_args = json.loads(tool_call.function.arguments)

                        logging.info(f"🔧 Executing tool: {function_name} with args: {function_args}")

                        # Route to the right provider (IGDB or URL reader)
                        function_result = await self._dispatch_tool(function_name, function_args, self._last_author(messages) or "")

                        logging.info(f"🔧 Tool result: {type(function_result)} - {str(function_result)[:200]}...")

                        messages.append(
                            {
                                "role": "tool",
                                "tool_call_id": tool_call.id,
                                # IGDB text is external input — sanitize before prompting (SAF-03)
                                "content": (
                                    sanitize_external_text(json.dumps(function_result), 8000) if function_result else "No results found"
                                ),
                            }
                        )

                    # Get final response after function execution - remove tools for final call
                    final_chat_kwargs = {
                        "model": model,
                        "messages": messages,
                        "response_format": ENVELOPE_RESPONSE_FORMAT,
                    }
                    logging.debug(f"🔧 Sending final request to OpenAI with {len(messages)} messages (no tools)")
                    logging.debug(f"🔧 Last few messages: {messages[-3:] if len(messages) > 3 else messages}")

                    final_result = await openai_chat(self.client, **final_chat_kwargs)
                    self._record_usage(final_result)
                    answer_obj = final_result.choices[0].message

                    logging.debug(
                        f"🔧 Final OpenAI response: content_length={len(answer_obj.content) if answer_obj.content else 0}, has_tool_calls={hasattr(answer_obj, 'tool_calls') and answer_obj.tool_calls}"
                    )
                    if answer_obj.content:
                        logging.debug(f"🔧 Response preview: {answer_obj.content[:200]}")
                    else:
                        logging.warning(f"🔧 OpenAI returned NULL content despite {final_result.usage.completion_tokens} completion tokens")

                    # If OpenAI returns null content after function calling, use empty string
                    if not answer_obj.content and function_result:
                        logging.warning("OpenAI returned null after function calling, using empty string")
                        answer_obj.content = ""
                except Exception as e:
                    # If function calling fails, fall back to regular response
                    logging.warning(f"Function calling failed, using regular response: {e}")
                    answer_obj = message
            else:
                answer_obj = message

            # Handle null content from OpenAI
            content = answer_obj.content
            if content is None:
                logging.warning("OpenAI returned null content, using empty string")
                content = ""

            answer = {"content": content, "role": answer_obj.role}
            self.rate_limit_backoff = exponential_backoff()
            self._use_retry_model = False
            logging.info(f"generated response {result.usage}: {repr(answer)}")
            return answer, limit
        except openai.BadRequestError as err:
            if "maximum context length is" in str(err) and limit > 4:
                logging.warning(f"context length exceeded, reduce the limit {limit}: {str(err)}")
                limit -= 1
                return None, limit
            raise err
        except openai.RateLimitError as err:
            rate_limit_sleep = next(self.rate_limit_backoff)
            self._use_retry_model = True
            logging.warning(f"got an rate limit error, sleep for {rate_limit_sleep} seconds: {str(err)}")
            await asyncio.sleep(rate_limit_sleep)
        except Exception as err:
            import traceback

            logging.warning(f"failed to generate response: {repr(err)}")
            logging.debug(f"Full traceback: {traceback.format_exc()}")
        return None, limit

    async def edit_openai(self, description: str, paths: List[Any], count: int = 1) -> List[BytesIO]:
        """Edit/remix from cached inputs, ≤4 files (IMG-13)."""
        if not self.ledger.budget_ok():
            raise RuntimeError("daily budget exhausted - refusing image edit")
        model = self.config.get("image-model", "gpt-image-2")
        handles = [open(path, "rb") for path in paths[:4]]
        try:
            response = await openai_image_edit(
                self.client,
                model=model,
                image=handles if len(handles) > 1 else handles[0],
                prompt=description,
                n=max(1, min(int(count), 4)),
                size=self.config.get("image-size", "1024x1024"),
            )
        finally:
            for handle in handles:
                handle.close()
        buffers = [BytesIO(base64.b64decode(item.b64_json)) for item in response.data]
        self.ledger.add_images(len(buffers))
        logging.info(f"edited {len(buffers)} image(s) on {model} from {len(handles)} input(s)")
        return buffers

    async def propose_task(self) -> Optional[Dict[str, Any]]:
        """One follow-up proposal from recent episodes on memory-model (TSK-08)."""
        if "memory-model" not in self.config or self.store is None or not self.ledger.budget_ok():
            return None
        channel = self.config.get("chat-channel", "chat")
        episodes = await asyncio.to_thread(self.store.recent_episodes, channel, 5)
        if not episodes:
            return None
        episode_lines = "\n".join(f"- {episode}" for episode in episodes)
        messages = [
            {"role": "system", "content": TASKGEN_SYSTEM},
            {"role": "user", "content": f"Recent conversation summaries in #{channel}:\n{episode_lines}"},
        ]
        try:
            result = await openai_chat(
                self.client, model=self.config["memory-model"], messages=messages, response_format=TASKGEN_RESPONSE_FORMAT
            )
            self._record_usage(result)
            return json.loads(result.choices[0].message.content)
        except Exception as err:
            logging.warning(f"task proposal failed: {repr(err)}")
        return None

    async def classify(self, message: Any, history_tail: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
        """~100-token reply/factual/emoji verdict on classifier-model (BEH-01/03)."""
        if "classifier-model" not in self.config or not self.ledger.budget_ok():
            return None
        tail = "\n".join(str(entry.get("content", ""))[:300] for entry in history_tail[-6:])
        messages = [
            {"role": "system", "content": CLASSIFIER_SYSTEM},
            {"role": "user", "content": f"Recent chat:\n{tail}\n\nLAST message:\n{str(message)}"},
        ]
        try:
            result = await openai_chat(
                self.client, model=self.config["classifier-model"], messages=messages, response_format=CLASSIFIER_RESPONSE_FORMAT
            )
            self._record_usage(result)
            return json.loads(result.choices[0].message.content)
        except Exception as err:
            logging.warning(f"classifier failed - failing open: {repr(err)}")
        return None

    async def consolidate(self, observations: List[Dict[str, Any]], known_facts: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
        """Batched memory consolidation on memory-model (MEM-02)."""
        if "memory-model" not in self.config or not self.ledger.budget_ok():
            return None
        observation_lines = "\n".join(f"[{obs['kind']}] {obs['user']}: {obs['content']}" for obs in observations)
        known_lines = "\n".join(f"- {fact['user']}: {fact['fact']}" for fact in known_facts) or "(none)"
        messages = [
            {"role": "system", "content": CONSOLIDATION_SYSTEM},
            {"role": "user", "content": f"Known facts:\n{known_lines}\n\nObservation log:\n{observation_lines}"},
        ]
        try:
            result = await openai_chat(
                self.client, model=self.config["memory-model"], messages=messages, response_format=CONSOLIDATION_RESPONSE_FORMAT
            )
            self._record_usage(result)
            parsed = json.loads(result.choices[0].message.content)
            logging.info(f"memory consolidation: {len(parsed.get('facts', []))} new facts, episode={bool(parsed.get('episode'))}")
            return parsed
        except Exception as err:
            logging.warning(f"memory consolidation failed: {repr(err)}")
        return None

    async def _execute_igdb_function(self, function_name: str, function_args: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Execute IGDB function calls from OpenAI.
        """
        logging.info(f"🎮 _execute_igdb_function called: {function_name}")

        if not self.igdb:
            logging.error("🎮 IGDB function called but self.igdb is None!")
            return {"error": "IGDB not available"}

        try:
            if function_name == "search_games":
                query = function_args.get("query", "")
                limit = function_args.get("limit", 5)

                logging.info(f"🎮 Searching IGDB for: '{query}' (limit: {limit})")

                if not query:
                    logging.warning("🎮 No search query provided to search_games")
                    return {"error": "No search query provided"}

                results = await asyncio.to_thread(self.igdb.search_games, query, limit)
                logging.info(f"🎮 IGDB search returned: {len(results) if results and isinstance(results, list) else 0} results")

                if results and isinstance(results, list) and len(results) > 0:
                    return {"games": results}
                else:
                    return {"games": [], "message": f"No games found matching '{query}'"}

            elif function_name == "get_games_by_release_date":
                year = function_args.get("year")
                month = function_args.get("month")
                platform = function_args.get("platform")
                limit = function_args.get("limit", 10)

                logging.info(
                    f"🎮 Searching IGDB for games releasing in {year}/{month or 'all'} on {platform or 'all platforms'} (limit: {limit})"
                )

                if not year:
                    logging.warning("🎮 No year provided to get_games_by_release_date")
                    return {"error": "No year provided"}

                results = await asyncio.to_thread(self.igdb.get_games_by_release_date, year, month, platform, limit)
                logging.info(
                    f"🎮 IGDB release date search returned: {len(results) if results and isinstance(results, list) else 0} results"
                )

                if results and isinstance(results, list) and len(results) > 0:
                    return {"games": results}
                else:
                    period = f"{year}/{month}" if month else str(year)
                    platform_text = f" on {platform}" if platform else ""
                    return {"games": [], "message": f"No games found releasing in {period}{platform_text}"}

            elif function_name == "get_games_by_platform":
                platform = function_args.get("platform", "")
                genre = function_args.get("genre")
                limit = function_args.get("limit", 10)

                logging.info(f"🎮 Searching IGDB for games on {platform} {f'in {genre} genre' if genre else ''} (limit: {limit})")

                if not platform:
                    logging.warning("🎮 No platform provided to get_games_by_platform")
                    return {"error": "No platform provided"}

                results = await asyncio.to_thread(self.igdb.get_games_by_platform, platform, genre, limit)
                logging.info(f"🎮 IGDB platform search returned: {len(results) if results and isinstance(results, list) else 0} results")

                if results and isinstance(results, list) and len(results) > 0:
                    return {"games": results}
                else:
                    genre_text = f" in {genre} genre" if genre else ""
                    return {"games": [], "message": f"No games found for {platform}{genre_text}"}

            elif function_name == "get_game_details":
                game_id = function_args.get("game_id")

                logging.info(f"🎮 Getting IGDB details for game ID: {game_id}")

                if not game_id:
                    logging.warning("🎮 No game ID provided to get_game_details")
                    return {"error": "No game ID provided"}

                result = await asyncio.to_thread(self.igdb.get_game_details, game_id)
                logging.info(f"🎮 IGDB game details returned: {bool(result)}")

                if result:
                    return {"game": result}
                else:
                    return {"error": f"Game with ID {game_id} not found"}
            else:
                return {"error": f"Unknown function: {function_name}"}

        except Exception as e:
            logging.error(f"Error executing IGDB function {function_name}: {e}")
            return {"error": f"Failed to execute {function_name}: {str(e)}"}