22-tool architecture: log.query, user.ask, journal system deployed

New tools implemented and deployed to dev gateway:
- log.query: focused event queries (chat/deaths/joins/actions), replaces 200-line dump
- user.ask: risk-scaled clarifying questions, async with tellraw
- journal.read/write: per-player files, cross-mode (God+Sudo share)

All wired into langgraph_gateway.py _execute_tool and model-driven tool loop.
Tool schemas updated (22 total). Deployed to CT 644 dev server.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

agent/tools/user_ask.py

@@ -0,0 +1,79 @@
+"""
+User Ask — clarifying questions sent to the player in-game.
+
+The model sends a question via tellraw and the gateway stores the pending
+question state. The player's next chat message gets routed back as the
+tool result.
+
+Risk-scaled: model should exhaust journal/state/log queries before asking.
+Low risk = just act creatively. High risk = ask first.
+
+Implementation:
+  1. Model emits: <tool_call>{"name": "user.ask", "arguments": {"question": "..."}}</tool_call>
+  2. Gateway sends tellraw to the player
+  3. Gateway stores pending_question in session state
+  4. Player's next chat message becomes the tool result
+  5. Model continues with the answer
+
+For training: simulate the ask/answer flow with synthetic responses.
+For production: gateway handles the async wait.
+
+Usage:
+    from agent.tools.user_ask import handle_user_ask, format_ask_tellraw
+"""
+
+import json
+from typing import Any, Dict
+
+
+def format_ask_tellraw(player: str, question: str, prefix: str = "[MORTDECAI]") -> str:
+    """Format a clarifying question as a tellraw command."""
+    safe_q = question.replace('"', '\\"').replace("\\", "\\\\")
+    return (
+        f'tellraw {player} ['
+        f'{{"text":"{prefix} ","color":"gold","bold":true}},'
+        f'{{"text":"{safe_q}","color":"yellow","italic":true}}'
+        f']'
+    )
+
+
+def handle_user_ask(config: dict, arguments: dict, rcon_fn=None) -> Dict[str, Any]:
+    """
+    Send a clarifying question to the player.
+
+    In production: sends tellraw and returns a pending state.
+    The gateway is responsible for waiting for the player's response
+    and feeding it back as the tool result.
+
+    In training: the response is simulated in the training data.
+
+    Args:
+        config: server config
+        arguments: {"player": str, "question": str}
+        rcon_fn: function to execute RCON commands
+
+    Returns:
+        {"ok": True, "status": "pending", "question": question}
+        In production, the gateway replaces this with the actual player response.
+    """
+    player = arguments.get("player", "")
+    question = arguments.get("question", "")
+
+    if not player or not question:
+        return {"ok": False, "error": "player and question required"}
+
+    # Send the question in-game
+    if rcon_fn:
+        prefix = config.get("god_chat_prefix", "[MORTDECAI]")
+        cmd = format_ask_tellraw(player, question, prefix)
+        try:
+            rcon_fn(cmd)
+        except Exception as e:
+            return {"ok": False, "error": f"Failed to send question: {e}"}
+
+    return {
+        "ok": True,
+        "status": "pending",
+        "player": player,
+        "question": question,
+    }