MCP Standards

# MCP Standards - System Architecture **Last Updated**: 2025-10-14 --- ## Executive Summary MCP Standards is a self-learning AI standards system that automatically learns from corrections and updates configuration files: 1. **MindsDB** - Federated data access across 200+ sources via single MCP server 2. **Strata** - Progressive tool discovery and intelligent routing 3. **SQLite Knowledge Engine** - Local-first memory with full-text search This architecture eliminates the complexity of managing dozens of MCP servers while providing enterprise-grade data access, intelligent tool selection, and persistent knowledge storage. --- ## Core Architecture ``` ┌─────────────────────────────────────────────────────────────┐ │ Claude Code/Desktop │ │ (User Interface Layer) │ └─────────────────────┬───────────────────────────────────────┘ │ ┌───────┼───────┐ │ │ │ ▼ ▼ ▼ ┌────────┐ ┌─────┐ ┌──────────┐ │MindsDB │ │Strata│ │ Local │ │ MCP │ │ MCP │ │Knowledge │ │Server │ │Router│ │ Engine │ └────┬───┘ └──┬──┘ └────┬─────┘ │ │ │ │ │ ▼ │ │ ┌─────────┐ │ │ │SQLite DB│ │ │ │+FTS5 │ │ │ └─────────┘ │ │ │ ▼ │ ┌─────────────┐ │ │Progressive │ │ │Tool │ │ │Discovery │ │ └─────────────┘ │ ▼ ┌─────────────────┐ │ Federated Data │ │ • 200+ Sources │ │ • Real-time │ │ • SQL Interface │ └─────────────────┘ ``` --- ## Component Architecture ### 1. MindsDB MCP Server (Data Federation Layer) **Purpose**: Single unified gateway for all data access needs **Capabilities**: - **200+ Data Sources**: Databases, SaaS apps, APIs, file systems - **Federated Queries**: Cross-source joins and analytics - **AI-Native Operations**: Vector search, embeddings, ML models - **Enterprise Security**: Audit trails, governance, performance optimization **Key Tools**: ```json { "list_databases": "Discover available data sources", "query": "Execute federated queries across all sources", "create_model": "Deploy ML models for predictions", "create_knowledge_base": "Setup RAG with vector search" } ``` **Configuration**: ```json { "mindsdb": { "command": "docker", "args": ["run", "-p", "47334:47334", "mindsdb/mindsdb"] } } ``` ### 2. Strata MCP Router (Tool Discovery Layer) **Purpose**: Progressive tool discovery without context overload **Discovery Pattern**: ``` Intent → Integration → Category → Action ``` **Benefits**: - **Scalable**: Handles thousands of tools without token explosion - **Progressive**: Step-by-step drill-down prevents overwhelm - **Intelligent**: Context-aware tool recommendation - **Multi-App**: Seamless workflows across applications **Configuration**: ```json { "strata": { "command": "npx", "args": ["-y", "strata-mcp"] } } ``` ### 3. Local Knowledge Engine (Memory Layer) **Purpose**: Persistent knowledge storage with full-text search **Components**: - **SQLite Database**: Single-file storage with FTS5 search - **Automatic Logging**: Captures significant tool executions - **Semantic Extraction**: Converts actions to knowledge episodes - **Cross-Session Memory**: Persistent context across interactions **Schema**: ```sql CREATE TABLE episodes ( id INTEGER PRIMARY KEY, name TEXT NOT NULL, content TEXT NOT NULL, source TEXT NOT NULL, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP, metadata JSON ); CREATE VIRTUAL TABLE episodes_fts USING fts5( name, content, source, content=episodes ); ``` --- ## Data Flow Architecture ### 1. Knowledge Capture Flow ``` User Action → Tool Execution → Semantic Extractor → SQLite Storage ↓ ↓ ↓ ↓ Claude Code → MCP Tool Call → Knowledge Episode → Local Memory ``` ### 2. Query Resolution Flow ``` User Query → Local Search → MindsDB Federation → Strata Discovery ↓ ↓ ↓ ↓ Natural → SQLite FTS → Cross-Source Query → Progressive Tools Language Results Real-time Data Guided Selection ``` ### 3. Tool Orchestration Flow ``` Intent → Strata Router → Tool Selection → MindsDB Execution → Memory Update ↓ ↓ ↓ ↓ ↓ "Research" → Categories → Specific Tool → Data Query → Episode Stored ``` --- ## Configuration Strategy ### Unified MCP Configuration **Claude Desktop** (`~/Library/Application Support/Claude/mcp.json`): ```json { "mcpServers": { "mindsdb": { "command": "docker", "args": ["run", "-p", "47334:47334", "mindsdb/mindsdb"] }, "strata": { "command": "npx", "args": ["-y", "strata-mcp"] }, "local-memory": { "command": "uv", "args": ["--directory", "./mcp-servers/local-memory", "run", "server.py"] } } } ``` **Claude Desktop Config** (`~/Library/Application Support/Claude/claude_desktop_config.json`): ```json { "projects": { "/path/to/your/project": { "mcpServers": { "mindsdb": { "type": "stdio", "command": "docker", "args": ["run", "-p", "47334:47334", "mindsdb/mindsdb"] }, "strata": { "type": "stdio", "command": "npx", "args": ["-y", "strata-mcp"] }, "local-memory": { "type": "stdio", "command": "uv", "args": ["--directory", "./mcp-servers/local-memory", "run", "server.py"] } } } } } ``` ### Scoped Configuration **Project-Level** (`.mcp.json`): ```json { "mcpServers": { "local-memory": { "type": "stdio", "command": "uv", "args": ["--directory", "./mcp-servers/local-memory", "run", "server.py"], "env": { "DB_PATH": "./knowledge.db" } } } } ``` --- ## Security and Governance ### Data Security 1. **Local-First**: SQLite knowledge stored locally, no cloud dependency 2. **Federated Security**: MindsDB handles data source authentication 3. **Permission-Based**: Strata enforces tool access controls 4. **Audit Trails**: All operations logged for compliance ### Enterprise Controls ```json { "permissions": { "allow": [ "mindsdb:query:read", "strata:discover:*", "local-memory:*" ], "deny": [ "mindsdb:model:create" ], "ask": [ "mindsdb:database:create" ] } } ``` --- ## Performance Characteristics ### Scalability Metrics | Component | Scale | Performance | Memory | |-----------|-------|-------------|---------| | MindsDB | 200+ sources | Real-time queries | ~500MB | | Strata | 1000+ tools | Progressive discovery | ~50MB | | SQLite | 1M+ episodes | Sub-second FTS | ~100MB | ### Optimization Strategies 1. **Intelligent Caching**: MindsDB caches frequent queries 2. **Progressive Loading**: Strata loads tools on-demand 3. **FTS Optimization**: SQLite indexes for fast search 4. **Connection Pooling**: Reuse database connections --- ## Integration Patterns ### Enterprise Integration ```python # Python SDK Usage from research_mcp import UnifiedMCP mcp = UnifiedMCP() # Query across all systems result = await mcp.query( "What changed in our Salesforce pipeline this week?", sources=["salesforce", "github", "local-memory"] ) # Progressive tool discovery tools = await mcp.discover_tools( intent="automate sales reporting", categories=["crm", "analytics"] ) ``` ### Agent Orchestration ```python # Multi-agent coordination from research_mcp.agents import ResearchAgent, ActionAgent research = ResearchAgent(knowledge_base="local-memory") action = ActionAgent(tool_router="strata") # Coordinated workflow findings = await research.investigate("competitor analysis") actions = await action.plan_from_research(findings) ``` --- ## Migration Strategy ### Phase 1: Foundation (Week 1) - [ ] Setup MindsDB MCP server - [ ] Configure basic Strata routing - [x] Migrate to SQLite with FTS5 - [ ] Test unified configuration ### Phase 2: Integration (Week 2) - [ ] Connect primary data sources to MindsDB - [ ] Configure Strata tool categories - [ ] Implement automatic knowledge capture - [ ] Setup cross-system queries ### Phase 3: Optimization (Week 3) - [ ] Performance tuning and caching - [ ] Advanced security configuration - [ ] Agent orchestration patterns - [ ] Enterprise governance setup ### Phase 4: Scale (Week 4) - [ ] Multi-user configuration - [ ] Team knowledge sharing - [ ] Advanced analytics dashboards - [ ] Production monitoring --- ## Monitoring and Observability ### Health Checks ```bash # MCP Server Status curl http://localhost:47334/health # MindsDB /mcp list # Strata tools sqlite3 knowledge.db "SELECT COUNT(*) FROM episodes" # Local memory ``` ### Metrics Collection - **Query Performance**: Response times across data sources - **Tool Usage**: Most frequently discovered/used tools - **Knowledge Growth**: Episodes created per day/week - **Error Rates**: Failed queries and tool executions --- ## Future Roadmap ### Short-term (3 months) - Agent-to-Agent (A2A) protocol integration - Advanced RAG with vector embeddings - Real-time collaboration features - Mobile/web interface ### Medium-term (6 months) - Multi-tenant enterprise deployment - Advanced analytics and insights - Workflow automation engine - Third-party MCP server registry ### Long-term (12 months) - Federated knowledge graphs - AI-driven process optimization - Cross-organization knowledge sharing - Regulatory compliance automation --- ## Conclusion This unified architecture positions Research MCP as a comprehensive AI knowledge copilot that scales from individual use to enterprise deployment. By combining MindsDB's data federation, Strata's progressive tool discovery, and local SQLite knowledge storage, we create a system that is both powerful and practical. The architecture eliminates the complexity of managing multiple MCP servers while providing enterprise-grade capabilities for data access, tool discovery, and knowledge management. This foundation enables rapid development of sophisticated AI workflows while maintaining simplicity for everyday use.