AI Memory Layer MCP Server

Business and customer intelligence system with semantic search and relationship tracking.

What It Does

Stores and retrieves business information (products, services, locations, hours, policies, events) and customer data (behaviors, preferences, desires, rules, feedback) with AI-powered search.

Built on: HelixDB graph database with vector embeddings

Key Features

• Two Search Modes: Keyword search (fast, exact) and semantic search (meaning-based)
• Customer Interactions: Track product/service engagement with reasons
• Navigation System: Store directions with compass bearings and accessibility info
• Smart Updates: Automatically maintains search indexes when data changes
• Relationship Discovery: Find connections between customers and products/services

Quick Start

1. Prerequisites

• HelixDB server running (default: 127.0.0.1:6969)
• Rust 1.70+ (for building from source)

2. Configuration

Create mcpconfig.toml (see mcpconfig.example.toml):

[helix]
endpoint = "127.0.0.1"
port = 6969

[embedding]
# Option 1: Let HelixDB generate embeddings (recommended)
enabled = false

# Option 2: Generate embeddings yourself
enabled = true
provider = "openai"  # or "gemini", "local", "tcp"
model = "text-embedding-3-small"
openai_api_key = "sk-..."

3. Build & Run

cargo build --release
./target/release/helix-mcp-server

Or use pre-built binary from releases.

LLM Integration

Claude Desktop

%APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "ai-memory": {
      "command": "E:\\path\\to\\helix-mcp-server.exe"
    }
  }
}

Cursor / Windsurf

Add to MCP servers configuration:

{
  "mcpServers": {
    "ai-memory": {
      "command": "E:\\path\\to\\helix-mcp-server.exe"
    }
  }
}

Usage Examples

Store Business Info

LLM: create_business_memory(
  business_id: "biz123",
  memory_type: "product",
  data: {
    product_id: "prod_001",
    name: "Wireless Headphones",
    price: 99.99,
    description: "Noise-canceling bluetooth headphones"
  }
)

Search

# Keyword search (fast, exact)
LLM: search_bm25(
  query: "headphones",
  memory_types: ["products"]
)

# Semantic search (meaning-based)
LLM: search_semantic(
  query: "audio equipment for music lovers",
  memory_types: ["products"]
)

Track Customer Interaction

LLM: create_customer_product_interaction(
  customer_id: "cust456",
  product_id: "prod_001",
  interaction_id: "int_789",
  interaction_type: "purchased",
  text_reason: "Looking for quality headphones for commute"
)

Update with Auto-Refresh

LLM: update_business_memory(
  memory_id: "prod_001",
  memory_type: "product",
  updates: {
    composite_text: "Premium noise-canceling wireless headphones with 30hr battery"
  }
)
# Automatically updates search indexes

1. Filter results:

   Call filter_items(connection_id, {filter_spec}) → Returns filtered items
   

2. Search operations:

   Call search_keyword(connection_id, "query text", "label", 10)
   Call search_vector_text(connection_id, "search text", "vec_label")
   

Architecture

The server is structured as follows:

• main.rs - Server initialization and tool router setup
• helix_client.rs - HTTP client for HelixDB MCP endpoints
• tools.rs - MCP tool handler implementations
• session.rs - Session management (minimal, as HelixDB handles sessions)

Comparison with Python Implementation

This Rust implementation provides:

• ✅ All tools from helix-py MCP server
• ✅ Same API and behavior
• ✅ Compatible with all MCP clients
• ✅ Better performance and lower memory usage
• ✅ Standalone binary (no Python runtime needed)
• ⚠️ Note: search_vector (with embedder) not yet implemented - use search_vector_text instead

Troubleshooting

"Failed to connect to HelixDB"

Make sure HelixDB is running:

# Check if HelixDB is running
curl http://127.0.0.1:6969/health

Enable debug logging

$env:RUST_LOG="debug"
./helix-mcp-server

Available Tools (22 total)

Query & Search

• query_business_memory / query_customer_memory - Filter by criteria
• search_semantic - Find by meaning
• search_bm25 - Find by keywords (use for exact matches/IDs)
• find_customer_insights - Discover relationships

Create

• create_business_memory / create_customer_memory - Add memories
• create_customer_product_interaction / create_customer_service_interaction - Track interactions
• create_navigation_hub / create_navigation_waypoint / create_direction_path - Add directions

Update

• update_business_memory / update_customer_memory - Modify memories
• update_interaction / update_navigation - Modify interactions/directions

Query Specialized

• query_customer_interactions / search_customer_interactions - Find interactions
• query_navigation / search_navigation - Get directions

Delete

• delete_memory - Remove any memory type

Advanced

• do_query - Direct database queries (use primary tools first)

Search Strategy

1. Exact match? Use search_bm25 (product IDs, phone numbers, exact terms)
2. Conceptual? Use search_semantic (find similar products, related ideas)
3. Not sure? Try search_bm25 first, then search_semantic

Troubleshooting

Connection fails:

• Verify HelixDB is running: netstat -an | findstr :6969 (Windows)
• Check mcpconfig.toml settings

Search returns nothing:

• Try both search_bm25 and search_semantic
• Verify data exists with query_business_memory or query_customer_memory

LLM doesn't see tools:

• Restart LLM client after config changes
• Check server logs for errors

Architecture

• 22 unified tools route to 146+ database queries
• Smart routing by parameters (no tool explosion)
• Automatic search index maintenance on updates
• Two embedding modes: self-managed or HelixDB-generated

Documentation

• IMPLEMENTATION_COMPLETE.md - Full technical details
• QUICK_REFERENCE.md - Testing checklist and commands
• SYSTEM_VALIDATION.md - Current status report