Mode Manager MCP

"""Tools for managing memory optimization.""" from pathlib import Path from typing import Annotated, Optional from fastmcp import Context from ..memory_optimizer import MemoryOptimizer from ..server_registry import get_server_registry def register_memory_tools() -> None: """Register all memory optimization tools with the server.""" registry = get_server_registry() app = registry.app instruction_manager = registry.instruction_manager read_only = registry.read_only @app.tool( name="optimize_memory", description="Manually optimize a memory file using AI to reorganize and consolidate entries while preserving all information.", tags={"public", "memory"}, annotations={ "idempotentHint": False, "readOnlyHint": False, "title": "Optimize Memory File", "parameters": { "memory_file": "Optional path to specific memory file. If not provided, will optimize the user's main memory file.", "force": "Force optimization even if criteria are not met. Defaults to False.", }, "returns": "Returns detailed results of the optimization process including status, entries before/after, and backup information.", }, meta={ "category": "memory", }, ) async def optimize_memory( ctx: Context, memory_file: Annotated[Optional[str], "Path to memory file to optimize"] = None, force: Annotated[bool, "Force optimization regardless of criteria"] = False, ) -> str: """Manually optimize a memory file using AI sampling.""" if read_only: return "Error: Server is running in read-only mode" try: # Determine which file to optimize if memory_file: file_path = Path(memory_file) if not file_path.exists(): return f"Error: Memory file not found: {memory_file}" else: # Use default user memory file user_memory_path = instruction_manager.get_memory_file_path() if not user_memory_path.exists(): return "Error: No user memory file found to optimize" file_path = user_memory_path # Create optimizer and run optimization optimizer = MemoryOptimizer(instruction_manager) result = await optimizer.optimize_memory_if_needed(file_path, ctx, force=force) # Format result message status = result.get("status", "unknown") if status == "optimized": entries_before = result.get("entries_before", "unknown") entries_after = result.get("entries_after", "unknown") backup_created = result.get("backup_created", False) message = f"✅ Memory optimization completed successfully!\n" message += f"📊 Entries: {entries_before} → {entries_after}\n" message += f"🔄 Method: {result.get('method', 'ai')}\n" message += f"💾 Backup created: {'Yes' if backup_created else 'No'}\n" message += f"📝 Reason: {result.get('reason', 'Manual optimization')}" elif status == "metadata_updated": message = f"📝 Memory metadata updated (AI optimization unavailable)\n" message += f"💾 Backup created: {'Yes' if result.get('backup_created', False) else 'No'}\n" message += f"📝 Reason: {result.get('reason', 'Manual optimization')}" elif status == "skipped": message = f"⏭️ Optimization skipped: {result.get('reason', 'Unknown reason')}\n" message += f"💡 Use force=True to optimize anyway" elif status == "error": message = f"❌ Optimization failed: {result.get('reason', 'Unknown error')}" else: message = f"🔍 Optimization result: {status}" return message except Exception as e: return f"Error during memory optimization: {str(e)}" @app.tool( name="memory_stats", description="Get detailed statistics and optimization status for a memory file.", tags={"public", "memory"}, annotations={ "idempotentHint": True, "readOnlyHint": True, "title": "Memory File Statistics", "parameters": { "memory_file": "Optional path to specific memory file. If not provided, will show stats for the user's main memory file.", }, "returns": "Returns comprehensive statistics including file size, entry count, optimization eligibility, and configuration settings.", }, meta={ "category": "memory", }, ) def memory_stats( memory_file: Annotated[Optional[str], "Path to memory file to analyze"] = None, ) -> str: """Get detailed statistics about a memory file.""" try: # Determine which file to analyze if memory_file: file_path = Path(memory_file) if not file_path.exists(): return f"Error: Memory file not found: {memory_file}" else: # Use default user memory file user_memory_path = instruction_manager.get_memory_file_path() if not user_memory_path.exists(): return "No user memory file found" file_path = user_memory_path # Get stats optimizer = MemoryOptimizer(instruction_manager) stats = optimizer.get_memory_stats(file_path) if "error" in stats: return str(stats["error"]) # Format stats message message = f"📊 **Memory File Statistics**\n\n" message += f"📁 **File**: `{stats['file_path']}`\n" message += f"📏 **Size**: {stats['file_size_bytes']:,} bytes\n" message += f"📝 **Entries**: {stats['current_entries']}\n" message += f"🔄 **Last Optimized**: {stats['last_optimized'] or 'Never'}\n" message += f"⚡ **Optimization Version**: {stats['optimization_version']}\n\n" message += f"⚙️ **Configuration**:\n" message += f"• Auto-optimize: {'✅ Enabled' if stats['auto_optimize_enabled'] else '❌ Disabled'}\n" message += f"• Size threshold: {stats['size_threshold']:,} bytes\n" message += f"• Entry threshold: {stats['entry_threshold']} new entries\n" message += f"• Time threshold: {stats['time_threshold_days']} days\n\n" message += f"🎯 **Optimization Status**:\n" message += f"• Eligible: {'✅ Yes' if stats['optimization_eligible'] else '❌ No'}\n" message += f"• Reason: {stats['optimization_reason']}\n" message += f"• New entries since last optimization: {stats['entries_since_last_optimization']}" return message except Exception as e: return f"Error getting memory stats: {str(e)}" @app.tool( name="configure_memory_optimization", description="Configure memory optimization settings for auto-optimization behavior.", tags={"public", "memory"}, annotations={ "idempotentHint": False, "readOnlyHint": False, "title": "Configure Memory Optimization", "parameters": { "memory_file": "Optional path to specific memory file. If not provided, will configure the user's main memory file.", "auto_optimize": "Enable or disable automatic optimization. True/False.", "size_threshold": "File size threshold in bytes for triggering optimization.", "entry_threshold": "Number of new entries to trigger optimization.", "time_threshold_days": "Number of days between optimizations.", }, "returns": "Returns confirmation of updated settings.", }, meta={ "category": "memory", }, ) def configure_memory_optimization( memory_file: Annotated[Optional[str], "Path to memory file to configure"] = None, auto_optimize: Annotated[Optional[bool], "Enable/disable auto-optimization"] = None, size_threshold: Annotated[Optional[int], "Size threshold in bytes"] = None, entry_threshold: Annotated[Optional[int], "Entry count threshold"] = None, time_threshold_days: Annotated[Optional[int], "Time threshold in days"] = None, ) -> str: """Configure memory optimization settings.""" if read_only: return "Error: Server is running in read-only mode" try: # Determine which file to configure if memory_file: file_path = Path(memory_file) if not file_path.exists(): return f"Error: Memory file not found: {memory_file}" else: # Use default user memory file user_memory_path = instruction_manager.get_memory_file_path() if not user_memory_path.exists(): return "Error: No user memory file found to configure" file_path = user_memory_path # Read current frontmatter from ..simple_file_ops import parse_frontmatter_file, write_frontmatter_file frontmatter, content = parse_frontmatter_file(file_path) # Update settings updated_settings = [] if auto_optimize is not None: frontmatter["autoOptimize"] = auto_optimize updated_settings.append(f"auto_optimize: {auto_optimize}") if size_threshold is not None: frontmatter["sizeThreshold"] = size_threshold updated_settings.append(f"size_threshold: {size_threshold:,} bytes") if entry_threshold is not None: frontmatter["entryThreshold"] = entry_threshold updated_settings.append(f"entry_threshold: {entry_threshold}") if time_threshold_days is not None: frontmatter["timeThreshold"] = time_threshold_days updated_settings.append(f"time_threshold: {time_threshold_days} days") if not updated_settings: return "No settings provided to update. Available options: auto_optimize, size_threshold, entry_threshold, time_threshold_days" # Write updated frontmatter success = write_frontmatter_file(file_path, frontmatter, content, create_backup=True) if success: message = "✅ Memory optimization settings updated:\n" for setting in updated_settings: message += f"• {setting}\n" message += f"\n💾 Backup created for safety" return message else: return "❌ Failed to update memory optimization settings" except Exception as e: return f"Error configuring memory optimization: {str(e)}"