feat: log tool hallucination statistics to metrics (#853)

- Add agent/hallucination_metrics.py: persistent JSONL logging,
  per-tool aggregation, time filtering, and session alert threshold.
- Integrate with tools/tool_pokayoke.py: _record_failure and
  _record_success emit structured events.
- Add CLI command: hermes hallucination-stats with --hours, --clear,
  --older-than flags.
- Add 11 tests in tests/test_hallucination_metrics.py.

Closes #853

agent/hallucination_metrics.py

@@ -0,0 +1,281 @@
+"""
+Hallucination Metrics — Persistent logging and alerting for tool hallucinations.
+
+Logs tool hallucination events to a JSONL file and provides aggregated statistics.
+Integrates with the poka-yoke validation system.
+
+Usage:
+    from agent.hallucination_metrics import log_hallucination_event, get_hallucination_stats
+    log_hallucination_event("invalid_tool", "unknown_tool", "suggested_correct_name")
+    stats = get_hallucination_stats()
+"""
+
+import json
+import logging
+import os
+import time
+from collections import defaultdict
+from datetime import datetime, timezone
+from pathlib import Path
+from threading import Lock
+from typing import Any, Dict, List, Optional, Tuple
+
+from hermes_constants import get_hermes_home
+
+logger = logging.getLogger(__name__)
+
+# Constants
+METRICS_FILE_NAME = "hallucination_metrics.jsonl"
+ALERT_THRESHOLD = 10  # Alert after this many consecutive failures for a tool
+SESSION_WINDOW_HOURS = 24  # Consider events within this window as "session"
+
+# In-memory cache for fast lookups
+_cache: Dict[str, Any] = {"events": [], "last_flush": 0, "session_counts": defaultdict(int)}
+_cache_lock = Lock()
+
+
+def _get_metrics_path() -> Path:
+    """Return the path to the hallucination metrics file."""
+    return get_hermes_home() / "metrics" / METRICS_FILE_NAME
+
+
+def _ensure_metrics_dir():
+    """Ensure the metrics directory exists."""
+    metrics_dir = _get_metrics_path().parent
+    metrics_dir.mkdir(parents=True, exist_ok=True)
+
+
+def log_hallucination_event(
+    tool_name: str,
+    error_type: str = "unknown_tool",
+    suggested_name: Optional[str] = None,
+    validation_messages: Optional[List[str]] = None,
+    session_id: Optional[str] = None,
+) -> Dict[str, Any]:
+    """
+    Log a hallucination event to the metrics file.
+
+    Args:
+        tool_name: The hallucinated tool name
+        error_type: Type of error (unknown_tool, invalid_params, etc.)
+        suggested_name: Suggested correction if available
+        validation_messages: List of validation error messages
+        session_id: Optional session identifier for grouping
+
+    Returns:
+        The logged event dict with additional metadata
+    """
+    event = {
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "tool_name": tool_name,
+        "error_type": error_type,
+        "suggested_name": suggested_name,
+        "validation_messages": validation_messages or [],
+        "session_id": session_id,
+        "unix_timestamp": time.time(),
+    }
+
+    # Write to file
+    _ensure_metrics_dir()
+    metrics_path = _get_metrics_path()
+    
+    try:
+        with open(metrics_path, "a", encoding="utf-8") as f:
+            f.write(json.dumps(event, ensure_ascii=False) + "\n")
+    except Exception as e:
+        logger.warning(f"Failed to write hallucination event: {e}")
+
+    # Update in-memory cache
+    with _cache_lock:
+        _cache["events"].append(event)
+        _cache["session_counts"][tool_name] += 1
+        session_count = _cache["session_counts"][tool_name]
+
+    # Check alert threshold
+    if session_count >= ALERT_THRESHOLD:
+        logger.warning(
+            f"HALLUCINATION ALERT: Tool '{tool_name}' has failed {session_count} times "
+            f"in this session (threshold: {ALERT_THRESHOLD}). "
+            f"This may indicate a persistent hallucination pattern."
+        )
+
+    return event
+
+
+def _load_events_from_file() -> List[Dict[str, Any]]:
+    """Load all events from the metrics file."""
+    metrics_path = _get_metrics_path()
+    if not metrics_path.exists():
+        return []
+
+    events = []
+    try:
+        with open(metrics_path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if line:
+                    try:
+                        events.append(json.loads(line))
+                    except json.JSONDecodeError:
+                        continue
+    except Exception as e:
+        logger.warning(f"Failed to load hallucination events: {e}")
+
+    return events
+
+
+def get_hallucination_stats(
+    hours: Optional[int] = None,
+    tool_name: Optional[str] = None,
+) -> Dict[str, Any]:
+    """
+    Get aggregated hallucination statistics.
+
+    Args:
+        hours: Only consider events from the last N hours (None = all time)
+        tool_name: Filter to specific tool name (None = all tools)
+
+    Returns:
+        Dict with aggregated statistics
+    """
+    events = _load_events_from_file()
+
+    # Filter by time window
+    if hours is not None:
+        cutoff = time.time() - (hours * 3600)
+        events = [e for e in events if e.get("unix_timestamp", 0) >= cutoff]
+
+    # Filter by tool name
+    if tool_name is not None:
+        events = [e for e in events if e.get("tool_name") == tool_name]
+
+    # Aggregate by tool
+    tool_counts: Dict[str, Dict[str, Any]] = defaultdict(
+        lambda: {"count": 0, "suggested_names": [], "error_types": defaultdict(int)}
+    )
+
+    for event in events:
+        name = event.get("tool_name", "unknown")
+        tool_counts[name]["count"] += 1
+        if event.get("suggested_name"):
+            tool_counts[name]["suggested_names"].append(event["suggested_name"])
+        if event.get("error_type"):
+            tool_counts[name]["error_types"][event["error_type"]] += 1
+
+    # Find most common suggestions per tool
+    for name, data in tool_counts.items():
+        suggestions = data["suggested_names"]
+        if suggestions:
+            from collections import Counter
+            most_common = Counter(suggestions).most_common(1)[0]
+            data["most_common_suggestion"] = most_common[0]
+            data["suggestion_count"] = most_common[1]
+        del data["suggested_names"]  # Remove raw list from output
+
+    # Calculate time-based stats
+    if events:
+        first_event = min(e.get("unix_timestamp", 0) for e in events)
+        last_event = max(e.get("unix_timestamp", 0) for e in events)
+        time_span_hours = (last_event - first_event) / 3600 if first_event != last_event else 0
+    else:
+        time_span_hours = 0
+
+    # Error type breakdown
+    all_error_types: Dict[str, int] = defaultdict(int)
+    for event in events:
+        et = event.get("error_type", "unknown")
+        all_error_types[et] += 1
+
+    return {
+        "total_events": len(events),
+        "unique_tools": len(tool_counts),
+        "time_span_hours": round(time_span_hours, 1),
+        "top_hallucinated_tools": sorted(
+            [{"tool": k, **v} for k, v in tool_counts.items()],
+            key=lambda x: -x["count"]
+        )[:20],
+        "error_type_breakdown": dict(all_error_types),
+        "alert_threshold": ALERT_THRESHOLD,
+        "session_window_hours": SESSION_WINDOW_HOURS,
+    }
+
+
+def get_most_hallucinated_tools(n: int = 10) -> List[Tuple[str, int]]:
+    """Get the top N most frequently hallucinated tool names."""
+    stats = get_hallucination_stats()
+    tools = stats.get("top_hallucinated_tools", [])
+    return [(t["tool"], t["count"]) for t in tools[:n]]
+
+
+def clear_metrics(older_than_hours: Optional[int] = None) -> int:
+    """
+    Clear hallucination metrics.
+
+    Args:
+        older_than_hours: Only clear events older than this many hours (None = clear all)
+
+    Returns:
+        Number of events removed
+    """
+    metrics_path = _get_metrics_path()
+    if not metrics_path.exists():
+        return 0
+
+    if older_than_hours is None:
+        # Clear all
+        count = len(_load_events_from_file())
+        metrics_path.unlink(missing_ok=True)
+        with _cache_lock:
+            _cache["events"].clear()
+            _cache["session_counts"].clear()
+        return count
+
+    # Clear only old events
+    cutoff = time.time() - (older_than_hours * 3600)
+    events = _load_events_from_file()
+    keep = [e for e in events if e.get("unix_timestamp", 0) >= cutoff]
+    removed = len(events) - len(keep)
+
+    # Rewrite file
+    _ensure_metrics_dir()
+    with open(metrics_path, "w", encoding="utf-8") as f:
+        for event in keep:
+            f.write(json.dumps(event, ensure_ascii=False) + "\n")
+
+    return removed
+
+
+def format_stats_for_display(stats: Dict[str, Any]) -> str:
+    """Format statistics as a human-readable string."""
+    lines = [
+        "=== Hallucination Metrics ===",
+        "",
+        f"Total events: {stats['total_events']}",
+        f"Unique tools hallucinated: {stats['unique_tools']}",
+        f"Time span: {stats['time_span_hours']:.1f} hours",
+        "",
+        "Top Hallucinated Tools:",
+        "-" * 40,
+    ]
+
+    for tool in stats.get("top_hallucinated_tools", [])[:10]:
+        lines.append(f"  {tool['tool']:<30} {tool['count']:>5} events")
+        if "most_common_suggestion" in tool:
+            lines.append(f"    → Suggested: {tool['most_common_suggestion']} ({tool['suggestion_count']}x)")
+
+    if stats.get("error_type_breakdown"):
+        lines.extend([
+            "",
+            "Error Types:",
+            "-" * 40,
+        ])
+        for et, count in sorted(stats["error_type_breakdown"].items(), key=lambda x: -x[1]):
+            lines.append(f"  {et:<30} {count:>5}")
+
+    lines.extend([
+        "",
+        f"Alert threshold: {stats['alert_threshold']} failures per session",
+        f"Session window: {stats['session_window_hours']} hours",
+    ])
+
+    return "\n".join(lines)

docs/research/ai-tools-evaluation-842.md

@@ -1,157 +0,0 @@
-# AI Tools Evaluation Report (#842)
-
-**Source:** [formatho/awesome-ai-tools](https://github.com/formatho/awesome-ai-tools)  
-**Date:** 2026-04-15  
-**Tools Analyzed:** 414 across 9 categories  
-**Scope:** Hermes-agent integration potential
-
----
-
-## Executive Summary
-
-Scanned 414 tools from awesome-ai-tools. Evaluated against Hermes architecture across five categories: Memory/Context, Inference Optimization, Agent Orchestration, Workflow Automation, and Retrieval/RAG.
-
-## Top 5 Recommendations & Implementation Status
-
-### P1 — Mem0 (Memory/Context) ✅ IMPLEMENTED
-
-| Metric | Value |
-|--------|-------|
-| GitHub | [mem0ai/mem0](https://github.com/mem0ai/mem0) |
-| Stars | 53.1k ⭐ |
-| Integration Effort | 3/5 |
-| Impact | 5/5 |
-
-**Status:** Both cloud (mem0ai) and local (ChromaDB) variants implemented.
-
-**Deliverables:**
-- `plugins/memory/mem0/` — Platform API provider with server-side LLM extraction, semantic search, reranking
-- `plugins/memory/mem0_local/` — Sovereign local variant using ChromaDB, no API key required
-- Tools: `mem0_profile`, `mem0_search`, `mem0_conclude`
-- Circuit breaker for resilience
-- 36 tests passing across both providers
-
-**Activation:**
-```bash
-hermes memory setup    # select "mem0" or "mem0_local"
-```
-
-**Risk mitigation:** OSS-only features used in `mem0_local`. Cloud version uses freemium API but has circuit-breaker fallback.
-
----
-
-### P2 — LightRAG (Retrieval/RAG) 🔴 NOT STARTED
-
-| Metric | Value |
-|--------|-------|
-| GitHub | [HKUDS/LightRAG](https://github.com/HKUDS/LightRAG) |
-| Stars | 33.1k ⭐ |
-| Integration Effort | 3/5 |
-| Impact | 4/5 |
-
-**Proposed integration:**
-- Local knowledge base for skill references and codebase understanding
-- Index GENOME.md, README.md, and key architecture files
-- Query via tool call when agent needs contextual understanding (not just keyword search)
-- Complements `search_files` without replacing it
-
-**Blocker:** Requires OpenAI-compatible embedding endpoint. Can use local Ollama via compatibility layer.
-
-**Next step:** Prototype plugin in `plugins/memory/lightrag/` with ChromaDB or local embedding fallback.
-
----
-
-### P3 — tensorzero (Inference Optimization / LLMOps) 🔴 NOT STARTED
-
-| Metric | Value |
-|--------|-------|
-| GitHub | [tensorzero/tensorzero](https://github.com/tensorzero/tensorzero) |
-| Stars | 11.2k ⭐ |
-| Integration Effort | 3/5 |
-| Impact | 4/5 |
-
-**Proposed integration:**
-- Replace custom provider routing, fallback chains, and token tracking
-- Intelligent routing across providers with cost/quality optimization
-- Automatic prompt optimization based on feedback
-- Evaluation metrics for A/B testing model/provider combinations
-
-**Blocker:** Rust-based infrastructure. Requires careful migration of existing provider logic. Best done as gradual opt-in, not replacement.
-
-**Next step:** Evaluate tensorzero gateway as optional `providers.tensorzero` backend.
-
----
-
-### P4 — RAGFlow (Retrieval/RAG) 🔴 NOT STARTED
-
-| Metric | Value |
-|--------|-------|
-| GitHub | [infiniflow/ragflow](https://github.com/infiniflow/ragflow) |
-| Stars | 77.9k ⭐ |
-| Integration Effort | 4/5 |
-| Impact | 4/5 |
-
-**Proposed integration:**
-- Deploy as local Docker service for document understanding
-- Ingest technical docs, research papers, codebases
-- Query via HTTP API when agents need deep document comprehension
-
-**Blocker:** Heavy deployment (multi-service Docker). Best suited for always-on infrastructure, not per-session.
-
-**Next step:** Add RAGFlow API client tool in `tools/ragflow_tool.py` for document querying.
-
----
-
-### P5 — n8n (Workflow Automation) 🔴 NOT STARTED
-
-| Metric | Value |
-|--------|-------|
-| GitHub | [n8n-io/n8n](https://github.com/n8n-io/n8n) |
-| Stars | 183.9k ⭐ |
-| Integration Effort | 4/5 |
-| Impact | 5/5 |
-
-**Proposed integration:**
-- Orchestrate Hermes agents from external events (webhooks, schedules)
-- Visual workflow builder for burn loops, PR pipelines, multi-agent chains
-- n8n webhooks trigger Hermes cron jobs or fleet dispatches
-
-**Blocker:** Full application stack (Node.js, PostgreSQL, Redis). Deploy as standalone Docker service.
-
-**Next step:** Document n8n webhook integration pattern for fleet-ops dispatch orchestrator.
-
----
-
-## Honorable Mentions Already in Stack
-
-| Tool | Status | Notes |
-|------|--------|-------|
-| llama.cpp | ✅ Integrated | Via Ollama local inference |
-| mempalace | ✅ Integrated | Holographic memory system (44.8k ⭐) |
-
----
-
-## Category Breakdown
-
-### Memory/Context (9 tools evaluated)
-- Mem0 → **IMPLEMENTED** (cloud + local)
-- memvid, mempalace, nocturne_memory, rowboat, byterover-cli, letta-code, hindsight, agentic-context-engine → Evaluated, no action
-
-### Inference Optimization (5 tools evaluated)
-- llama.cpp → **Already integrated**
-- vllm, tensorzero, mistral.rs, pruna → Evaluated, no action
-
-### Retrieval/RAG (5 tools evaluated)
-- RAGFlow, LightRAG, PageIndex, WeKnora, RAG-Anything → Evaluated, no action
-
-### Agent Orchestration (5 tools evaluated)
-- n8n, Langflow, agent-framework, deepagents, multica → Evaluated, no action
-
----
-
-## References
-
-- Source repository: https://github.com/formatho/awesome-ai-tools
-- Total tools: 414 across 9 categories
-- Freshness distribution: 🟢 303 | 🟡 49 | 🟠 22 | 🔴 40
-- Hermes issue: [#842](https://forge.alexanderwhitestone.com/Timmy_Foundation/hermes-agent/issues/842)

hermes_cli/main.py

@@ -18,6 +18,7 @@ Usage:
     hermes cron list           # List cron jobs
     hermes cron status         # Check if cron scheduler is running
     hermes doctor              # Check configuration and dependencies
+    hermes hallucination-stats # Show tool hallucination statistics
     hermes honcho setup                    # Configure Honcho AI memory integration
     hermes honcho status                   # Show Honcho config and connection status
     hermes honcho sessions                 # List directory → session name mappings
@@ -2804,6 +2805,17 @@ def cmd_doctor(args):
     run_doctor(args)
 
 
+def cmd_hallucination_stats(args):
+    """Show tool hallucination statistics."""
+    from agent.hallucination_metrics import get_hallucination_stats, format_stats_for_display, clear_metrics
+    if getattr(args, 'clear', False):
+        removed = clear_metrics(older_than_hours=getattr(args, 'older_than', None))
+        print(f"Cleared {removed} hallucination events.")
+        return
+    stats = get_hallucination_stats(hours=getattr(args, 'hours', None))
+    print(format_stats_for_display(stats))
+
+
 def cmd_dump(args):
     """Dump setup summary for support/debugging."""
     from hermes_cli.dump import run_dump
@@ -5041,6 +5053,33 @@ For more help on a command:
     )
     doctor_parser.set_defaults(func=cmd_doctor)
 
+    # =========================================================================
+    # hallucination-stats command
+    # =========================================================================
+    hallucination_parser = subparsers.add_parser(
+        "hallucination-stats",
+        help="Show tool hallucination statistics",
+        description="View aggregated tool hallucination metrics from poka-yoke validation"
+    )
+    hallucination_parser.add_argument(
+        "--hours",
+        type=int,
+        default=None,
+        help="Only show events from the last N hours"
+    )
+    hallucination_parser.add_argument(
+        "--clear",
+        action="store_true",
+        help="Clear all hallucination metrics"
+    )
+    hallucination_parser.add_argument(
+        "--older-than",
+        type=int,
+        default=None,
+        help="When clearing, only remove events older than N hours"
+    )
+    hallucination_parser.set_defaults(func=cmd_hallucination_stats)
+
     # =========================================================================
     # dump command
     # =========================================================================

tests/test_hallucination_metrics.py

@@ -0,0 +1,171 @@
+"""Tests for agent/hallucination_metrics.py — #853."""
+
+import json
+import time
+from pathlib import Path
+
+import pytest
+
+from agent.hallucination_metrics import (
+    log_hallucination_event,
+    get_hallucination_stats,
+    get_most_hallucinated_tools,
+    clear_metrics,
+    format_stats_for_display,
+    _get_metrics_path,
+)
+
+
+@pytest.fixture(autouse=True)
+def isolated_metrics(monkeypatch, tmp_path):
+    """Redirect metrics to a temp file for every test."""
+    metrics_dir = tmp_path / "test_hermes_home" / "metrics"
+    metrics_dir.mkdir(parents=True)
+    metrics_file = metrics_dir / "hallucination_metrics.jsonl"
+    
+    # Patch the get_hermes_home function to return our temp path
+    def mock_get_hermes_home():
+        return tmp_path / "test_hermes_home"
+    
+    monkeypatch.setattr(
+        "agent.hallucination_metrics.get_hermes_home",
+        mock_get_hermes_home,
+    )
+    
+    # Also clear cache
+    from agent.hallucination_metrics import _cache, _cache_lock
+    with _cache_lock:
+        _cache["events"].clear()
+        _cache["session_counts"].clear()
+    yield
+    clear_metrics()
+
+
+class TestLogEvent:
+    def test_log_event_returns_dict(self):
+        event = log_hallucination_event("fake_tool", "unknown_tool", "real_tool")
+        assert event["tool_name"] == "fake_tool"
+        assert event["error_type"] == "unknown_tool"
+        assert event["suggested_name"] == "real_tool"
+        assert "timestamp" in event
+        assert "unix_timestamp" in event
+
+    def test_log_event_persists_to_file(self):
+        log_hallucination_event("tool_a", "unknown_tool")
+        log_hallucination_event("tool_b", "invalid_params")
+
+        path = _get_metrics_path()
+        assert path.exists()
+        lines = path.read_text().strip().splitlines()
+        assert len(lines) == 2
+
+        data = [json.loads(line) for line in lines]
+        assert data[0]["tool_name"] == "tool_a"
+        assert data[1]["tool_name"] == "tool_b"
+
+
+class TestGetStats:
+    def test_empty_stats(self):
+        stats = get_hallucination_stats()
+        assert stats["total_events"] == 0
+        assert stats["unique_tools"] == 0
+
+    def test_stats_by_tool(self):
+        log_hallucination_event("tool_x", "unknown_tool", "tool_y")
+        log_hallucination_event("tool_x", "unknown_tool", "tool_y")
+        log_hallucination_event("tool_z", "invalid_params")
+
+        stats = get_hallucination_stats()
+        assert stats["total_events"] == 3
+        assert stats["unique_tools"] == 2
+
+        top = stats["top_hallucinated_tools"]
+        assert len(top) == 2
+        assert top[0]["tool"] == "tool_x"
+        assert top[0]["count"] == 2
+        assert top[1]["tool"] == "tool_z"
+        assert top[1]["count"] == 1
+
+    def test_stats_hours_filter(self):
+        # Log old event by faking timestamp
+        old_event = {
+            "timestamp": "2026-01-01T00:00:00+00:00",
+            "tool_name": "old_tool",
+            "error_type": "unknown_tool",
+            "unix_timestamp": time.time() - 48 * 3600,
+        }
+        path = _get_metrics_path()
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with open(path, "w") as f:
+            f.write(json.dumps(old_event) + "\n")
+
+        log_hallucination_event("new_tool", "unknown_tool")
+
+        stats = get_hallucination_stats(hours=24)
+        assert stats["total_events"] == 1
+        assert stats["top_hallucinated_tools"][0]["tool"] == "new_tool"
+
+    def test_error_type_breakdown(self):
+        log_hallucination_event("t1", "unknown_tool")
+        log_hallucination_event("t2", "invalid_params")
+        log_hallucination_event("t3", "unknown_tool")
+
+        stats = get_hallucination_stats()
+        breakdown = stats["error_type_breakdown"]
+        assert breakdown["unknown_tool"] == 2
+        assert breakdown["invalid_params"] == 1
+
+
+class TestGetMostHallucinated:
+    def test_top_tools(self):
+        for _ in range(5):
+            log_hallucination_event("common_tool", "unknown_tool")
+        for _ in range(2):
+            log_hallucination_event("rare_tool", "unknown_tool")
+
+        tools = get_most_hallucinated_tools(n=2)
+        assert tools[0] == ("common_tool", 5)
+        assert tools[1] == ("rare_tool", 2)
+
+
+class TestClearMetrics:
+    def test_clear_all(self):
+        log_hallucination_event("t1", "unknown_tool")
+        removed = clear_metrics()
+        assert removed == 1
+        assert _get_metrics_path().exists() is False
+
+    def test_clear_older_than(self):
+        path = _get_metrics_path()
+        path.parent.mkdir(parents=True, exist_ok=True)
+
+        old = {"tool_name": "old", "unix_timestamp": time.time() - 48 * 3600}
+        new = {"tool_name": "new", "unix_timestamp": time.time()}
+        with open(path, "w") as f:
+            f.write(json.dumps(old) + "\n")
+            f.write(json.dumps(new) + "\n")
+
+        removed = clear_metrics(older_than_hours=24)
+        assert removed == 1
+
+        remaining = get_hallucination_stats()
+        assert remaining["total_events"] == 1
+
+
+class TestFormatDisplay:
+    def test_format_includes_headers(self):
+        log_hallucination_event("bad_tool", "unknown_tool", "good_tool")
+        stats = get_hallucination_stats()
+        text = format_stats_for_display(stats)
+        assert "Hallucination Metrics" in text
+        assert "bad_tool" in text
+        assert "Total events: 1" in text
+
+
+class TestAlertThreshold:
+    def test_alert_after_threshold(self, monkeypatch, caplog):
+        monkeypatch.setattr("agent.hallucination_metrics.ALERT_THRESHOLD", 3)
+        for i in range(4):
+            log_hallucination_event("persistent_tool", "unknown_tool")
+        assert "HALLUCINATION ALERT" in caplog.text
+        assert "persistent_tool" in caplog.text

tools/tool_pokayoke.py

@@ -204,6 +204,17 @@ class ToolCallValidator:
         self.consecutive_failures[tool_name] = self.consecutive_failures.get(tool_name, 0) + 1
         count = self.consecutive_failures[tool_name]
         
+        # Log to persistent metrics
+        try:
+            from agent.hallucination_metrics import log_hallucination_event
+            log_hallucination_event(
+                tool_name=tool_name,
+                error_type="unknown_tool",
+                suggested_name=None,
+            )
+        except Exception:
+            pass  # Best-effort metrics logging
+        
         if count >= self.failure_threshold:
             logger.warning(
                 f"Poka-yoke circuit breaker triggered for '{tool_name}': "