Personal Knowledge Assistant

#!/usr/bin/env python3 """ Personal Knowledge Assistant MCP Server This is the main entry point for the Personal Knowledge Assistant MCP server. It provides tools for managing emails, social media, projects, and personal data insights. """ import asyncio from typing import Any, Dict, List, Optional, Sequence import logging import structlog from mcp.server import Server from mcp.server.stdio import stdio_server from mcp.types import ( Resource, Tool, TextContent, ImageContent, EmbeddedResource, ) # Configure structured logging structlog.configure( processors=[ structlog.stdlib.filter_by_level, structlog.stdlib.add_logger_name, structlog.stdlib.add_log_level, structlog.stdlib.PositionalArgumentsFormatter(), structlog.processors.CallsiteParameterAdder( parameters=[structlog.processors.CallsiteParameter.FILENAME, structlog.processors.CallsiteParameter.LINENO] ), structlog.processors.TimeStamper(fmt="iso"), structlog.dev.ConsoleRenderer() ], wrapper_class=structlog.stdlib.BoundLogger, logger_factory=structlog.stdlib.LoggerFactory(), cache_logger_on_first_use=True, ) logger = structlog.get_logger() # Initialize MCP Server server = Server("personal-knowledge-assistant") # Tool definitions following MCP protocol TOOL_DEFINITIONS = [ Tool( name="send_email", description="Send emails through configured email provider with smart composition assistance", inputSchema={ "type": "object", "properties": { "to": { "type": "array", "items": {"type": "string", "format": "email"}, "description": "Recipient email addresses" }, "subject": { "type": "string", "description": "Email subject line" }, "body": { "type": "string", "description": "Email body content" }, "cc": { "type": "array", "items": {"type": "string", "format": "email"}, "description": "CC recipients (optional)" }, "bcc": { "type": "array", "items": {"type": "string", "format": "email"}, "description": "BCC recipients (optional)" }, "attachments": { "type": "array", "items": {"type": "string"}, "description": "File paths for attachments (optional)" } }, "required": ["to", "subject", "body"] } ), Tool( name="analyze_email_patterns", description="Analyze email communication patterns, response times, and relationship insights", inputSchema={ "type": "object", "properties": { "timeframe": { "type": "string", "enum": ["week", "month", "quarter", "year"], "description": "Analysis timeframe", "default": "month" }, "contacts": { "type": "array", "items": {"type": "string", "format": "email"}, "description": "Specific contacts to analyze (optional)" }, "analysis_type": { "type": "string", "enum": ["patterns", "response_times", "frequency", "sentiment"], "description": "Type of analysis to perform", "default": "patterns" } }, "required": [] } ), Tool( name="post_social_media", description="Create and post content to various social media platforms with optimal timing", inputSchema={ "type": "object", "properties": { "platforms": { "type": "array", "items": { "type": "string", "enum": ["twitter", "linkedin", "facebook", "instagram"] }, "description": "Target social media platforms" }, "content": { "type": "string", "description": "Post content" }, "media_urls": { "type": "array", "items": {"type": "string", "format": "uri"}, "description": "Media attachments (optional)" }, "schedule_time": { "type": "string", "format": "date-time", "description": "Schedule post for specific time (optional)" }, "hashtags": { "type": "array", "items": {"type": "string"}, "description": "Hashtags to include (optional)" } }, "required": ["platforms", "content"] } ), Tool( name="analyze_social_engagement", description="Analyze social media engagement metrics, trends, and audience insights", inputSchema={ "type": "object", "properties": { "platforms": { "type": "array", "items": { "type": "string", "enum": ["twitter", "linkedin", "facebook", "instagram"] }, "description": "Platforms to analyze" }, "timeframe": { "type": "string", "enum": ["day", "week", "month", "quarter"], "description": "Analysis timeframe", "default": "week" }, "metrics": { "type": "array", "items": { "type": "string", "enum": ["engagement", "reach", "impressions", "clicks", "shares"] }, "description": "Specific metrics to analyze", "default": ["engagement", "reach"] } }, "required": ["platforms"] } ), Tool( name="manage_project_context", description="Manage project contexts, tasks, and deadlines with intelligent prioritization", inputSchema={ "type": "object", "properties": { "action": { "type": "string", "enum": ["create", "update", "delete", "list", "prioritize"], "description": "Action to perform" }, "project_id": { "type": "string", "description": "Project identifier (required for update/delete)" }, "project_data": { "type": "object", "properties": { "name": {"type": "string"}, "description": {"type": "string"}, "deadline": {"type": "string", "format": "date-time"}, "priority": { "type": "string", "enum": ["low", "medium", "high", "urgent"] }, "tags": { "type": "array", "items": {"type": "string"} }, "status": { "type": "string", "enum": ["planning", "active", "on_hold", "completed", "cancelled"] } }, "description": "Project data (required for create/update)" } }, "required": ["action"] } ), Tool( name="track_personal_metrics", description="Track and analyze personal productivity metrics, habits, and goals", inputSchema={ "type": "object", "properties": { "metric_type": { "type": "string", "enum": ["productivity", "habits", "goals", "mood", "energy"], "description": "Type of metric to track" }, "action": { "type": "string", "enum": ["log", "analyze", "report", "trend"], "description": "Action to perform" }, "data": { "type": "object", "description": "Metric data to log (varies by metric_type)" }, "timeframe": { "type": "string", "enum": ["day", "week", "month", "quarter", "year"], "description": "Analysis timeframe for analyze/report/trend actions", "default": "week" } }, "required": ["metric_type", "action"] } ), Tool( name="generate_insights_report", description="Generate comprehensive insights reports combining data from all sources", inputSchema={ "type": "object", "properties": { "report_type": { "type": "string", "enum": ["daily_summary", "weekly_digest", "monthly_review", "custom"], "description": "Type of report to generate" }, "data_sources": { "type": "array", "items": { "type": "string", "enum": ["email", "social_media", "projects", "personal_metrics"] }, "description": "Data sources to include in report", "default": ["email", "social_media", "projects", "personal_metrics"] }, "format": { "type": "string", "enum": ["markdown", "html", "json", "pdf"], "description": "Report output format", "default": "markdown" }, "include_recommendations": { "type": "boolean", "description": "Include actionable recommendations", "default": true }, "custom_filters": { "type": "object", "description": "Custom filters for report data (optional)" } }, "required": ["report_type"] } ) ] @server.list_tools() async def handle_list_tools() -> List[Tool]: """List all available tools.""" logger.info("Listing available tools", tool_count=len(TOOL_DEFINITIONS)) return TOOL_DEFINITIONS @server.call_tool() async def handle_call_tool(name: str, arguments: Dict[str, Any]) -> Sequence[TextContent]: """Handle tool calls with proper MCP protocol compliance.""" logger.info("Tool called", tool_name=name, arguments=arguments) try: if name == "send_email": # Placeholder implementation - will be handled by api-integration-specialist return [TextContent( type="text", text=f"Email sending functionality not yet implemented. Would send to: {arguments.get('to')}" )] elif name == "analyze_email_patterns": # Placeholder implementation - will be handled by data-insights-analyst return [TextContent( type="text", text=f"Email pattern analysis not yet implemented. Analysis type: {arguments.get('analysis_type', 'patterns')}" )] elif name == "post_social_media": # Placeholder implementation - will be handled by api-integration-specialist return [TextContent( type="text", text=f"Social media posting not yet implemented. Platforms: {arguments.get('platforms')}" )] elif name == "analyze_social_engagement": # Placeholder implementation - will be handled by data-insights-analyst return [TextContent( type="text", text=f"Social media analysis not yet implemented. Platforms: {arguments.get('platforms')}" )] elif name == "manage_project_context": # Placeholder implementation - will be handled by data-insights-analyst return [TextContent( type="text", text=f"Project management not yet implemented. Action: {arguments.get('action')}" )] elif name == "track_personal_metrics": # Placeholder implementation - will be handled by data-insights-analyst return [TextContent( type="text", text=f"Personal metrics tracking not yet implemented. Type: {arguments.get('metric_type')}" )] elif name == "generate_insights_report": # Placeholder implementation - will be handled by data-insights-analyst return [TextContent( type="text", text=f"Insights report generation not yet implemented. Type: {arguments.get('report_type')}" )] else: logger.error("Unknown tool called", tool_name=name) return [TextContent( type="text", text=f"Unknown tool: {name}" )] except Exception as e: logger.error("Tool execution failed", tool_name=name, error=str(e)) return [TextContent( type="text", text=f"Tool execution failed: {str(e)}" )] @server.list_resources() async def handle_list_resources() -> List[Resource]: """List available resources.""" # Placeholder - resources will be implemented by other agents logger.info("Listing resources") return [] async def main(): """Main entry point for the MCP server.""" logger.info("Starting Personal Knowledge Assistant MCP Server") try: # Run the server using stdio transport async with stdio_server() as (read_stream, write_stream): await server.run( read_stream, write_stream, server.create_initialization_options() ) except Exception as e: logger.error("Server failed to start", error=str(e)) raise if __name__ == "__main__": # Run the async main function asyncio.run(main())