moai-integration-mcp

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

---

Quick Start Workflow

Universal MCP Server Setup:

```python

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:

```bash

# 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

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

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

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

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

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

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

---

Multi-Service Orchestration

Design-to-Code Pipeline:

```python

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": documentation,

"design_tokens": design_data["design_tokens"],

"workflow_status": "completed"

}

```

Knowledge Base Automation:

```python

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:

```python

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))

```

---

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

---

Design System Integration

```python

# 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

```python

# 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

```python

# 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

})

```

---

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.