moai-integration-mcp

name	moai-integration-mcp
description	Universal MCP integration specialist combining Figma, Notion, Nano-Banana, and custom connectors with comprehensive orchestration patterns
version	1.0.0
category	integration
tags	["mcp","figma","notion","nano-banana","connectors","orchestration"]
updated	"2025-11-30T00:00:00.000Z"
status	active
author	MoAI-ADK Team

MCP Integration Specialist

Quick Reference (30 seconds)

Universal MCP Integration - Comprehensive MCP (Model Context Protocol) specialist combining Figma design integration, Notion knowledge management, Nano-Banana AI services, and custom connector frameworks with advanced orchestration capabilities.

Core Capabilities:

• Figma Integration: Design system extraction, component generation, token synchronization
• Notion Integration: Database queries, page management, knowledge extraction
• Nano-Banana AI: Content generation, analysis, AI-powered workflows
• Universal Connectors: Extensible framework for custom service integrations
• Multi-Service Orchestration: Complex workflows across multiple platforms
• Enterprise Security: OAuth, credential management, secure authentication

When to Use:

• Integrating multiple external services via MCP
• Building automated design-to-code workflows
• Creating AI-powered content pipelines
• Implementing cross-platform data synchronization
• Developing custom MCP connectors

---

Implementation Guide (5 minutes)

Quick Start Workflow

Universal MCP Server Setup:

from moai_integration_mcp import UniversalMCPServer, ServiceOrchestrator

# Initialize universal MCP server
mcp_server = UniversalMCPServer("integration-server")

# Configure connectors
mcp_server.setup_connectors({
 'figma': {'api_key': os.getenv('FIGMA_TOKEN')},
 'notion': {'api_key': os.getenv('NOTION_TOKEN')},
 'nano_banana': {'api_key': os.getenv('NANO_BANANA_TOKEN')}
})

# Register orchestration tools
orchestrator = ServiceOrchestrator(mcp_server)
orchestrator.register_workflows()

# Start server
mcp_server.start(port=3000)

Multi-Service Workflow:

# Design system automation
mcp-tools design_to_code --figma-file "abc123" --output ./src/components

# Knowledge extraction workflow
mcp-tools knowledge_extraction --notion-db "xyz789" --analyze "best_practices"

# AI-powered content generation
mcp-tools ai_workflow --input "./docs/" --output "./generated/" --model "claude-3-5-sonnet"

Core Components

1. Server Architecture (modules/server-architecture.md)

• Universal MCP server framework
• Multi-connector management
• Dynamic tool registration
• Configuration and initialization

2. Integration Patterns (modules/integration-patterns.md)

• Multi-service orchestration
• Workflow engine and templates
• Data transformation pipelines
• Advanced integration patterns

3. Security & Authentication (modules/security-authentication.md)

• OAuth 2.0 flows for all services
• Secure credential storage
• Token management and refresh
• Access control and permissions

4. Error Handling (modules/error-handling.md)

• Circuit breaker patterns
• Retry logic with backoff
• Fault tolerance mechanisms
• Monitoring and observability

---

Advanced Patterns (10+ minutes)

Multi-Service Orchestration

Design-to-Code Pipeline:

async def complete_design_workflow(figma_file_id: str, target_library: str = "shadcn"):
 """Complete design system to production code workflow."""

 # Phase 1: Extract design data
 design_data = await mcp_server.invoke_tool("extract_figma_components", {
 "file_id": figma_file_id,
 "include_tokens": True
 })

 # Phase 2: Process with AI
 component_specs = []
 for component in design_data["components"]:
 spec = await mcp_server.invoke_tool("analyze_with_ai", {
 "content": json.dumps(component),
 "analysis_type": "component_specification"
 })
 component_specs.append(spec)

 # Phase 3: Generate code
 generated_components = []
 for spec in component_specs:
 code = await mcp_server.invoke_tool("generate_ai_content", {
 "prompt": f"Generate React component for: {spec['analysis']}",
 "max_tokens": 3000
 })
 generated_components.append(code)

 # Phase 4: Create documentation
 documentation = await mcp_server.invoke_tool("generate_ai_content", {
 "prompt": f"Create documentation for components: {json.dumps(component_specs)}",
 "max_tokens": 4000
 })

 return {
 "components": generated_components,
 : documentation,
 : design_data[],
 : 
 }

Knowledge Base Automation:

async def knowledge_base_workflow(notion_database: str, analysis_goals: list):
 """Automated knowledge extraction and organization workflow."""

 # Extract content from Notion
 content = await mcp_server.invoke_tool("query_notion_database", {
 "database_id": notion_database,
 "query": {"filter": {"property": "Status", "select": {"equals": "Published"}}}
 })

 # Analyze with AI for each goal
 analyses = {}
 for goal in analysis_goals:
 analysis = await mcp_server.invoke_tool("analyze_with_ai", {
 "content": json.dumps(content["results"]),
 "analysis_type": goal
 })
 analyses[goal] = analysis

 # Structure knowledge base
 structured_kb = await mcp_server.invoke_tool("generate_ai_content", {
 "prompt": f"Create structured knowledge base from analyses: {json.dumps(analyses)}",
 "max_tokens": 5000
 })

 return {
 "raw_content": content,
 "analyses": analyses,
 "structured_knowledge": structured_kb,
 "source_count": len(content["results"])
 }

Custom Connector Development

Extensible Connector Framework:

class CustomConnector:
 def __init__(self, service_config: dict):
 self.config = service_config
 self.client = None

 async def initialize(self):
 """Initialize custom service client."""
 self.client = CustomServiceClient(self.config)

 def register_tools(self, server):
 """Register connector-specific tools."""

 @server.tool()
 async def custom_service_operation(
 operation_type: str,
 parameters: dict = {}
 ) -> dict:
 """Execute operation on custom service."""
 try:
 result = await self.client.execute_operation(
 operation_type,
 parameters
 )

 return {
 "status": "success",
 "result": result,
 "operation": operation_type
 }

 except Exception as e:
 return {
 "status": "error",
 "error": str(e),
 "operation": operation_type
 }

# Register custom connector
mcp_server.register_connector('custom_service', CustomConnector(config))

---

Works Well With

Complementary Skills:

• moai-domain-frontend - Frontend component generation and integration
• moai-domain-backend - Backend API integration patterns
• moai-docs-generation - Automated documentation workflows
• moai-foundation-claude - Claude Code integration patterns

External Services:

• Figma (design systems, component extraction)
• Notion (knowledge management, documentation)
• Nano-Banana (AI content generation)
• Custom APIs and web services
• Database systems and storage

Integration Platforms:

• FastMCP server framework
• OAuth 2.0 providers
• REST APIs and GraphQL
• Message queues and event systems
• Cloud storage services

---

Usage Examples

Design System Integration

# Extract and sync design tokens
tokens = await mcp_server.invoke_tool("sync_figma_tokens", {
 "file_id": "design-system-file",
 "output_format": "typescript",
 "include_variants": True
})

# Generate component library
components = await mcp_server.invoke_tool("extract_figma_components", {
 "file_id": "component-library",
 "target_framework": "react",
 "include_stories": True
})

Knowledge Base Management

# Extract and analyze knowledge
analysis = await mcp_server.invoke_tool("knowledge_extraction_workflow", {
 "notion_database_id": "knowledge-base",
 "analysis_goals": ["best_practices", "patterns", "action_items"],
 "output_format": "structured_json"
})

# Create new documentation
doc_page = await mcp_server.invoke_tool("create_notion_page", {
 "database_id": "documentation-db",
 "properties": {
 "Title": {"title": [{"text": {"content": "Best Practices Guide"}}]},
 "Category": {"select": {"name": "Guidelines"}}
 },
 "content": analysis["structured_knowledge"]
})

AI-Powered Workflows

# Generate content with AI
ai_content = await mcp_server.invoke_tool("generate_ai_content", {
 "prompt": "Create comprehensive API documentation",
 "model": "claude-3-5-sonnet",
 "max_tokens": 4000,
 "temperature": 0.7
})

# Analyze and summarize
summary = await mcp_server.invoke_tool("analyze_with_ai", {
 "content": ai_content["content"],
 "analysis_type": "summary",
 "include_key_points": True
})

---

Technology Stack

Core Framework:

• FastMCP (Python MCP server framework)
• AsyncIO for concurrent operations
• Pydantic for data validation
• HTTPX for HTTP client operations

Service Integrations:

• Figma API (design systems)
• Notion API (knowledge management)
• Nano-Banana API (AI services)
• Custom REST/GraphQL APIs

Security & Authentication:

• OAuth 2.0 implementation
• Cryptography for encryption
• JWT token management
• Secure credential storage

Error Handling & Reliability:

• Circuit breaker patterns
• Retry mechanisms with backoff
• Comprehensive error classification
• Monitoring and observability

Development Tools:

• Type hints and validation
• Comprehensive logging
• Performance monitoring
• Debugging and profiling tools

---

For detailed implementation patterns, connector development, and advanced workflows, see the modules/ directory.