Large memory.json : Memory Indexing

[akvoloh]

Request for an automated memory indexing system that dynamically analyzes MCP memory content and generates optimized index entities for efficient token usage and faster information retrieval.

Problem Statement

The use of MCP Memory is the primary source of notes and list items for several projects and IDE's. Current MCP memory systems suffer from token inefficiency when retrieving information:

• Large memory files (80k+ tokens) result in expensive queries
• Manual index creation is time-consuming and error-prone
• Generic search patterns don't leverage content structure
• No automatic optimization for common access patterns

Proposed Solution

Implement an indexing system that:

1. Content Analysis Engine

• Automatically scans all entities in memory.json
• Identifies content patterns and categories (Directives, project status, file locations, etc.)
• Extracts key information using pattern matching and keyword detection
• Builds content category maps based on actual memory content

2. Dynamic Index Generation

• Creates summary entities only for content categories that exist
• Generates optimized observations limited to essential information
• Produces consistent naming conventions for quick access commands
• Maintains backward compatibility with existing memory structure

3. Token Optimization

• Limits index entity size to <500 tokens each
• Creates hierarchical access patterns (summary → detailed → comprehensive)
• Implements content prioritization (most relevant information first)
• Provides progressive detail strategies for complex queries

4. Command Integration

• Generates standardized quick access commands (memrules, memstatus, etc.)
• Creates mapping between commands and specific index entities
• Implements fallback strategies for missing content
• Maintains consistent search patterns across projects

Technical Requirements

Core Components Needed:

1. Memory Parser: Read and parse JSON Lines format memory files
2. Pattern Analyzer: Detect content categories using configurable patterns
3. Index Generator: Create optimized summary entities dynamically
4. Command Mapper: Generate quick access command definitions
5. Output Writer: Write optimized memory files with indexes

Configuration Support:

• Configurable content patterns for different project types
• Adjustable token limits per index category
• Customizable command naming conventions
• Optional index categories based on project needs

Performance Targets:

• Generate indexes with <500 tokens each
• Achieve 95%+ token reduction for common queries
• Support memory files with 1000+ entities

Expected Benefits

For System Performance:

• Lower API costs due to reduced token usage
• Faster response times with targeted queries
• Improved scalability as memory files grow
• Better user experience with consistent performance

Compatibility Requirements

• Maintain full backward compatibility with existing memory.json format
• Support both JSON Lines and standard JSON input formats
• Work with existing MCP server implementations
• Preserve all existing entity relationships and metadata