1770 Database Design B11e9364

Database Design - MCP Gateway Registry

Overview

The MCP Gateway Registry supports three storage backends for data persistence:

1. File-Based Backend (Legacy, Backwards Compatible)

   • JSON file storage in the local filesystem
   • Maintained for backwards compatibility
   • Single-node deployments only
   • FAISS-based vector search

2. MongoDB CE (Local Development)

   • MongoDB Community Edition 8.2
   • Docker-based local deployment
   • Application-level vector search
   • Development and testing environments

3. AWS DocumentDB (Production, Recommended)

   • MongoDB-compatible managed service
   • Production-ready with clustering support
   • Native vector search with HNSW indexes
   • Multi-tenancy support via namespaces
   • Recommended for all production deployments

The default configuration for local development uses MongoDB CE, while production deployments use AWS DocumentDB.

Quick Architecture Reference

Application Services
        │
        ▼
Repository Factory (factory.py)
        │
        ├─> File Backend (legacy)
        │   └─> Local JSON files + FAISS
        │
        ├─> MongoDB CE (local dev)
        │   └─> Docker container + app-level vector search
        │
        └─> AWS DocumentDB (production)
            └─> Managed service + native vector search

Storage Backend Comparison

MongoDB CE & DocumentDB Architecture

For detailed information about the MongoDB and DocumentDB backends, see:

Storage Architecture: MongoDB CE & AWS DocumentDB

This comprehensive guide covers:

• MongoDB CE local development setup
• AWS DocumentDB production deployment
• Vector search implementation (app-level vs. native)
• Build and run process with build_and_run.sh
• Collection schemas and indexes
• Migration strategies
• Performance characteristics

Quick Summary

Collections (both MongoDB CE and DocumentDB):

All collections are suffixed with the configured namespace (e.g., _default, _production):

1. mcp_servers_{namespace} - Server definitions
2. mcp_agents_{namespace} - Agent cards
3. mcp_scopes_{namespace} - Authorization scopes
4. mcp_embeddings_1536_{namespace} - Vector embeddings
5. mcp_security_scans_{namespace} - Security scan results
6. mcp_federation_config_{namespace} - Federation configuration

Key Differences:

Collection Schemas

1. MCP Servers

Collection: mcp_servers_{namespace}

Stores MCP server definitions and metadata.

Document Structure:

{
  "_id": "/servers/financial-data",
  "server_name": "Financial Data Server",
  "description": "Provides stock market data and analysis",
  "path": "/servers/financial-data",
  "proxy_pass_url": "http://financial-server:8000",
  "supported_transports": ["stdio", "sse"],
  "auth_type": "oauth",
  "tags": ["finance", "data", "stocks"],
  "num_tools": 15,
  "tool_list": [
    {
      "name": "get_stock_price",
      "description": "Get current stock price",
      "schema": { /* JSON schema */ }
    }
  ],
  "is_enabled": true,
  "registered_at": "2026-01-03T10:00:00Z",
  "updated_at": "2026-01-03T12:30:00Z"
}

Indexes:

• path (unique) - Primary key
• is_enabled - Filter active servers
• tags - Tag-based filtering
• server_name - Text search

2. A2A Agents

Collection: mcp_agents_{namespace}

Stores Agent-to-Agent (A2A) agent cards and capabilities.

Document Structure:

{
  "_id": "/agents/financial-analyst",
  "protocol_version": "1.0",
  "name": "Financial Analysis Agent",
  "description": "Analyzes financial data and provides insights",
  "path": "/agents/financial-analyst",
  "url": "https://registry.example.com/agents/financial-analyst",
  "version": "2.1.0",
  "capabilities": ["analysis", "reporting", "forecasting"],
  "tags": ["finance", "analysis"],
  "is_enabled": true,
  "visibility": "public",
  "trust_level": "high",
  "registered_at": "2026-01-02T09:00:00Z",
  "updated_at": "2026-01-03T11:00:00Z"
}

Indexes:

• path (unique) - Primary key
• is_enabled - Filter active agents
• tags - Tag-based filtering
• name - Text search
• visibility - Access control

3. Authorization Scopes

Collection: mcp_scopes_{namespace}

Stores authorization scopes, permission mappings, and UI access control.

Document Types:

The scopes collection stores three document types, distinguished by scope_type:

Server Scope Document

{
  "_id": "scope:admin_access",
  "scope_type": "server_scope",
  "scope_name": "admin_access",
  "server_access": [
    {
      "server": "financial_server",
      "methods": ["GET", "POST", "PUT"],
      "tools": ["analyze_data", "generate_report"]
    }
  ],
  "description": "Full access to financial servers",
  "created_at": "2026-01-01T08:00:00Z",
  "updated_at": "2026-01-03T10:00:00Z"
}

Group Mapping Document

{
  "_id": "group:finance_team",
  "scope_type": "group_mapping",
  "group_name": "finance_team",
  "group_mappings": ["admin_access", "read_only_access"],
  "created_at": "2026-01-01T08:00:00Z",
  "updated_at": "2026-01-03T10:00:00Z"
}

UI Scope Document

{
  "_id": "ui:finance_team",
  "scope_type": "ui_scope",
  "scope_name": "finance_team",
  "ui_permissions": {
    "list_service": ["financial_server", "analytics_server"]
  },
  "created_at": "2026-01-01T08:00:00Z",
  "updated_at": "2026-01-03T10:00:00Z"
}

Indexes:

• _id (unique) - Primary key
• scope_type - Document type filter
• scope_name - Scope lookup
• group_name - Group lookup

4. Vector Embeddings

Collection: mcp_embeddings_{dimensions}_{namespace}

Example: mcp_embeddings_1536_default for 1536-dimensional embeddings

Stores vector embeddings for semantic search across servers and agents.

Document Structure:

{
  "_id": "/servers/financial-data",
  "entity_type": "mcp_server",
  "path": "/servers/financial-data",
  "name": "Financial Data Server",
  "description": "Provides stock market data and analysis",
  "tags": ["finance", "data"],
  "is_enabled": true,
  "text_for_embedding": "Financial Data Server. Provides stock market data and analysis. Tools: get_stock_price, analyze_portfolio",
  "embedding": [0.125, -0.342, 0.098, ...],  // 1536 floats
  "embedding_metadata": {
    "model": "amazon.titan-embed-text-v1",
    "provider": "litellm",
    "dimensions": 1536,
    "created_at": "2026-01-03T10:30:00Z"
  },
  "tools": [
    {"name": "get_stock_price", "description": "Get current stock price"}
  ],
  "metadata": { /* full server info */ },
  "indexed_at": "2026-01-03T10:30:00Z"
}

Indexes:

• path (unique) - Primary key
• entity_type - Filter by entity type
• embedding (vector) - DocumentDB only: HNSW vector index for fast similarity search

  // HNSW index configuration (DocumentDB)
  {
    "type": "hnsw",
    "similarity": "cosine",
    "dimensions": 1536,
    "m": 16,
    "efConstruction": 128
  }
  

Vector Search:

• MongoDB CE: Application-level cosine similarity in Python
• DocumentDB: Native HNSW index for sub-100ms queries

5. Security Scans

Collection: mcp_security_scans_{namespace}

Stores security vulnerability scan results.

Document Structure:

{
  "_id": "scan:financial_server:2026-01-03",
  "server_path": "/servers/financial-data",
  "scan_timestamp": "2026-01-03T14:00:00Z",
  "scan_status": "unsafe",
  "vulnerabilities": [
    {
      "severity": "high",
      "title": "SQL Injection vulnerability",
      "description": "User input not sanitized",
      "cve_id": "CVE-2024-12345",
      "package_name": "db-connector",
      "package_version": "2.1.0",
      "fixed_version": "2.1.5"
    }
  ],
  "risk_score": 0.75,
  "total_vulnerabilities": 2,
  "critical_count": 0,
  "high_count": 1,
  "medium_count": 1,
  "low_count": 0
}

Indexes:

• server_path - Lookup scans by server
• scan_status - Filter by status
• scan_timestamp (descending) - Get latest scans

6. Federation Config

Collection: mcp_federation_config_{namespace}

Stores federation configuration for external registries (Anthropic, ASOR).

Document Structure:

{
  "_id": "federation-config",
  "anthropic": {
    "enabled": true,
    "endpoint": "https://registry.modelcontextprotocol.io",
    "sync_on_startup": true,
    "servers": [
      {"name": "weather-service"},
      {"name": "news-aggregator"}
    ]
  },
  "asor": {
    "enabled": false,
    "endpoint": "https://asor-registry.example.com",
    "auth_env_var": "ASOR_AUTH_TOKEN",
    "sync_on_startup": false,
    "agents": []
  },
  "updated_at": "2026-01-03T12:00:00Z"
}

Indexes:

• _id (unique) - Single config per namespace

Vector Search Architecture

Embedding Generation

Module: registry/embeddings/

Supported Providers:

1. Sentence Transformers (Default, Local)

   • Model: all-MiniLM-L6-v2 (384 dimensions)
   • Runs locally, no API costs
   • Good for development

2. OpenAI (Cloud)

   • Model: text-embedding-ada-002 (1536 dimensions)
   • Requires API key
   • High quality embeddings

3. Amazon Bedrock Titan (Cloud)

   • Model: amazon.titan-embed-text-v1 (1536 dimensions)
   • Uses IAM authentication
   • AWS-native integration

Search Implementation

See: Storage Architecture: MongoDB CE & AWS DocumentDB for detailed search implementation.

Summary:

Hybrid Search

Both backends combine:

• Vector similarity (semantic matching) - Primary ranking
• Text matching (keyword boosting) - Secondary bonus

Formula:

final_score = vector_score + (text_boost * 0.03)

Where:
  vector_score = cosine_similarity(query_embedding, doc_embedding)  // 0-1
  text_boost = 3.0 (name match) + 2.0 (description match)           // 0-5

Configuration

Environment Variables

File: .env

# Storage Backend Selection
# Options:
#   "file" - JSON files (legacy)
#   "mongodb-ce" - MongoDB Community Edition (local dev)
#   "documentdb" - AWS DocumentDB (production)
STORAGE_BACKEND=mongodb-ce

# MongoDB/DocumentDB Connection
DOCUMENTDB_HOST=mongodb                    # Local: "mongodb", Prod: "cluster.docdb.amazonaws.com"
DOCUMENTDB_PORT=27017
DOCUMENTDB_DATABASE=mcp_registry
DOCUMENTDB_NAMESPACE=default               # Multi-tenancy: dev, staging, production

# Authentication (not needed for MongoDB CE)
DOCUMENTDB_USERNAME=admin
DOCUMENTDB_PASSWORD=secure_password

# TLS (MongoDB CE: false, DocumentDB: true)
DOCUMENTDB_USE_TLS=false
DOCUMENTDB_TLS_CA_FILE=global-bundle.pem
DOCUMENTDB_USE_IAM=false

# Replica Set
DOCUMENTDB_REPLICA_SET=rs0
DOCUMENTDB_READ_PREFERENCE=secondaryPreferred

# Embeddings Configuration
EMBEDDINGS_PROVIDER=sentence-transformers  # Or: litellm
EMBEDDINGS_MODEL_NAME=all-MiniLM-L6-v2    # Or: openai/text-embedding-ada-002
EMBEDDINGS_MODEL_DIMENSIONS=384            # Or: 1536

Initialization

MongoDB CE:

# Start MongoDB and initialize
docker compose up -d mongodb
docker compose up mongodb-init

# Verify
docker exec mcp-mongodb mongosh --eval "use mcp_registry; show collections"

AWS DocumentDB:

# Deploy with Terraform
cd terraform/aws-ecs
terraform apply

# Collections and indexes created automatically on first application startup

Repository Layer

All database operations go through repository interfaces defined in registry/repositories/interfaces.py:

• ServerRepositoryBase: Server CRUD operations
• AgentRepositoryBase: Agent card CRUD operations
• ScopeRepositoryBase: Authorization scope management
• SecurityScanRepositoryBase: Vulnerability scan storage
• FederationConfigRepositoryBase: Federation configuration
• SearchRepositoryBase: Vector search operations

Factory: registry/repositories/factory.py

The repository factory automatically selects the correct implementation based on STORAGE_BACKEND:

if backend in ["documentdb", "mongodb-ce"]:
    from .documentdb.server_repository import DocumentDBServerRepository
    return DocumentDBServerRepository()
else:
    from .file.server_repository import FileServerRepository
    return FileServerRepository()

Key Point: mongodb-ce and documentdb use the same repository code. The only difference is the connection configuration.

Migration from File Backend

To MongoDB CE (Local Development)

1. Update configuration:

   # In .env
   STORAGE_BACKEND=mongodb-ce
   

2. Start MongoDB:

   docker compose up -d mongodb
   docker compose up mongodb-init
   

3. Re-register servers and agents:

   # Use API to register from backup files
   for file in backup/*.json; do
       curl -X POST http://localhost:7860/servers \
           -H "Content-Type: application/json" \
           -d @"$file"
   done
   

To AWS DocumentDB (Production)

1. Deploy infrastructure:

   cd terraform/aws-ecs
   terraform apply
   

2. Update configuration:

   STORAGE_BACKEND=documentdb
   DOCUMENTDB_HOST=<cluster-endpoint>
   DOCUMENTDB_USERNAME=<username>
   DOCUMENTDB_PASSWORD=<password>
   DOCUMENTDB_USE_TLS=true
   

3. Import data:

   # Use mongodump/mongorestore or API
   mongorestore --host=<cluster> --ssl --db=mcp_registry ./backup
   

Performance Considerations

MongoDB CE (Local Development)

• Good for: <10,000 documents
• Search latency: 50-200ms (O(n) scan)
• Indexing: Fast document insertion
• Scaling: Limited to single container resources

AWS DocumentDB (Production)

• Good for: Millions of documents
• Search latency: 10-50ms (O(log n) HNSW)
• Indexing: Distributed across cluster
• Scaling: Horizontal (add read replicas), vertical (instance size)

Optimization Tips

1. Use appropriate instance sizes (DocumentDB)

   • db.r5.large for development
   • db.r5.xlarge or larger for production

2. Enable read replicas for high read throughput

3. Tune HNSW parameters (DocumentDB)

   • m=16, efConstruction=128 balances accuracy and speed
   • Increase for higher accuracy (slower)
   • Decrease for faster search (lower accuracy)

4. Monitor query patterns and create additional indexes as needed

See Also

• Storage Architecture: MongoDB CE & AWS DocumentDB - Comprehensive guide
• Database Abstraction Layer Design - Repository pattern details
• Embeddings Configuration - Vector embedding setup
• Configuration Guide - Full configuration reference
• MongoDB Documentation
• AWS DocumentDB Documentation