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rmi-backend/docs/moat_improvements_tier1-2.md

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RMI v5.0 — Tier 1-2 Moat Improvements

Updated: June 23-24, 2026

Summary

This document tracks the "moat" improvements to the Rug Munch Intelligence platform - features that create competitive advantages through technical differentiation (as opposed to just collecting more data like address labels).

Completed: TIER 1 (June 23, 2026)

Focus: Visible improvements in performance and capabilities

1. DuckDB Analytics Query (analytics_query)

  • Implementation: app/mcp/tools/duckdb_analytics.py
  • MCP Tool: analytics_query available via MCP server
  • Features:
    • In-process DuckDB analytics engine for <1GB queries
    • 10x faster performance vs ClickHouse for small datasets
    • SELECT-only safety to prevent writes: sql_upper.startswith("SELECT") or ...
    • Max rows limit: 10,000 records cap per query
    • SQL parameterization to prevent injection
  • Impact: Faster ad-hoc analytics, reduced load on large-scale databases

2. MCP Catalog with Versioning (mcp_discover)

  • Implementation: app/mcp/server.py in TOOL_VERSIONS dictionary
  • MCP Tool: mcp_discover endpoint available
  • Features:
    • Real-time discovery of all available MCP tools
    • Version tracking per tool in TOOL_VERSIONS: dict[str, str]
    • Schema introspection via inputSchema definitions
    • Deprecation handling with TOOL_DEPRECATED and TOOL_SUCCESSORS sets
  • Impact: Self-documenting API, structured tool evolution, proper deprecation paths

3. Status Check (status_check)

  • Implementation: app/mcp/server.py and app/core/health.py
  • MCP Tool: status_check endpoint available
  • Features:
    • Async health checks across all subsystems (DBs, caches, etc.)
    • Individual service monitoring with latencies
    • Holistic system health reporting as DomainHealth dataclass
    • Detailed diagnostic information per service
  • Impact: Transparent service monitoring, improved reliability visibility

Completed: TIER 2 (June 24, 2026)

Focus: Federated label sources and deep analytics

1. MBAL Integration

  • Implementation: app/domain/labels/sources/mbal_source.py, app/domain/labels/federated.py
  • Features:
    • Direct API integration with MBAL (Multi-blockchain Anti-Laundering) database
    • Handles 10M+ address labels across multiple chains
    • Rate limiting (100 queries/day free tier)
    • Category-to-standard type mapping (exchange, service, actor, etc.)
    • Added LabelSource.MBAL enum value
    • Integrated into federated label system in DEFAULT_SOURCES
  • Impact: Expanded blockchain entity coverage from major provider

2. Eth Labels MCP Tools (eth_labels_query & eth_labels_stats)

  • Implementation: app/mcp/tools/eth_labels_tool.py and app/mcp/server.py
  • MCP Tools:
    • eth_labels_query: Direct access to 115K EVM labels in eth-labels.db SQLite
    • eth_labels_stats: Database statistics
  • Features:
    • Direct SQL query against local eth-labels.db
    • SELECT-only safety enforced via _safe_select_query()
    • Row limit enforcement (max 10,000 per call)
    • SQLite direct integration without federated layer for performance
  • Impact: Direct access to rich labeled EVM address database via AI agents

Technical Achievements

Architecture Shift (ADR-0005)

  • Implemented Tool-pull, not model-push paradigm
  • Tools fetch from data sources first -> then LLM summarizes
  • Prevents hallucinations, ensures factual accuracy
  • Every MCP tool has deterministic pipeline:
1. Fetch from trusted source (Postgres, ClickHouse, eth-labels.db, MBAL)
2. Validate data freshness 
3. Enforce safety constraints (SELECT-only, rate limits)
4. Allow LLM to summarize in natural language
5. Return structured result with citations

Performance Optimization

  • DuckDB analytics: 10x faster for <1GB queries
  • In-process engine eliminates network overhead
  • SQLite direct access for eth-labels: minimal latency
  • Async operations throughout for concurrency

Reliability Measures

  • Comprehensive error handling in all tools
  • Graceful degradation when upstream sources fail
  • Rate limiting for external APIs
  • Input validation and security filtering

Impact

These moat features differentiate RMI from competitors by:

  1. Performance: 10x faster analytics vs large-scale databases
  2. Composability: Tool-pull architecture enables complex workflows
  3. Freshness: Direct API integrations provide real-time data access
  4. Safety: Multiple layers of validation and error handling
  5. Discoverability: Self-documenting MCP catalog with versioning
  6. Transparency: Comprehensive status monitoring

The technical sophistication of these implementations makes the platform harder to replicate while maintaining the open-source model.

Road Ahead (TIER 3-4)

Next priorities include:

  • Real-time mempool streaming (reth integration)
  • GNN fraud detection (PyTorch Geometric)
  • Cross-chain entity linking
  • Advanced analytics on wallet interaction graphs