Expert Registry MCP Server

"""Data models for Expert Registry MCP Server.""" from datetime import datetime from enum import Enum from typing import Dict, List, Optional, Any from pydantic import BaseModel, Field class TaskType(str, Enum): """Supported task types for expert selection.""" BUG_FIX = "bug-fix" INVESTIGATION = "investigation" REFACTORING = "refactoring" FEATURE = "feature" ARTICLE = "article" GENERAL = "general" class ExpertSpecialization(BaseModel): """Specialization details for an expert.""" technology: str frameworks: List[str] = Field(default_factory=list) expertise_level: str = "expert" class PerformanceMetrics(BaseModel): """Performance tracking for an expert.""" average_adherence_score: float = Field(ge=0.0, le=10.0) successful_applications: int = Field(ge=0) total_applications: int = Field(ge=0) last_used: Optional[datetime] = None class Expert(BaseModel): """Complete expert definition.""" id: str name: str version: str description: str domains: List[str] specializations: List[ExpertSpecialization] workflow_compatibility: Dict[str, float] = Field(default_factory=dict) performance_metrics: Optional[PerformanceMetrics] = None constraints: List[str] = Field(default_factory=list) patterns: List[str] = Field(default_factory=list) quality_standards: List[str] = Field(default_factory=list) tools_required: List[str] = Field(default_factory=list) created_at: datetime = Field(default_factory=datetime.now) updated_at: datetime = Field(default_factory=datetime.now) class ExpertRegistry(BaseModel): """Central expert registry.""" version: str last_updated: datetime experts: List[Expert] class TechnologyDetectionResult(BaseModel): """Result of technology detection.""" technologies: List[str] frameworks: List[str] confidence: float = Field(ge=0.0, le=1.0) detection_method: str = "auto" class ExpertScore(BaseModel): """Scoring breakdown for expert selection.""" expert_id: str total_score: float = Field(default=0.0, ge=0.0, le=1.0) technology_match: float = Field(default=0.0, ge=0.0, le=1.0) workflow_compatibility: float = Field(default=0.0, ge=0.0, le=1.0) performance_history: float = Field(default=0.0, ge=0.0, le=1.0) capability_assessment: float = Field(default=0.0, ge=0.0, le=1.0) semantic_similarity: Optional[float] = Field(None, ge=0.0, le=1.0) graph_connectivity: Optional[float] = Field(None, ge=0.0, le=1.0) class ExpertSelectionResult(BaseModel): """Result of expert selection process.""" expert: Expert score: ExpertScore reasoning: str alternatives: List[ExpertScore] = Field(default_factory=list) selection_strategy: str = "single" class ExpertContext(BaseModel): """Loaded expert context.""" expert_id: str content: str sections: Dict[str, str] = Field(default_factory=dict) loaded_at: datetime = Field(default_factory=datetime.now) class CapabilityAssessment(BaseModel): """Expert capability assessment for a task.""" expert_id: str task_description: str capability_score: float = Field(ge=0.0, le=1.0) confidence: float = Field(ge=0.0, le=1.0) reasoning: str assessed_at: datetime = Field(default_factory=datetime.now) class UsageTracking(BaseModel): """Track expert usage for analytics.""" expert_id: str task_id: str task_type: TaskType started_at: datetime completed_at: Optional[datetime] = None success: bool = False adherence_score: Optional[float] = Field(None, ge=0.0, le=10.0) error_message: Optional[str] = None class ExpertTeam(BaseModel): """Team of complementary experts.""" experts: List[Expert] coverage_score: float = Field(ge=0.0, le=1.0) synergy_score: float = Field(ge=0.0, le=1.0) reasoning: str class ExpertEmbedding(BaseModel): """Vector embeddings for an expert.""" expert_id: str embeddings: Dict[str, List[float]] = Field( description="Embeddings for different aspects (description, domains, technologies, patterns, constraints)" ) metadata: Dict[str, Any] = Field( description="Metadata about embeddings (model, timestamp, version)" )