MCP Code Analysis Server

"""Configuration models for MCP Code Analysis Server.""" import re from pathlib import Path from pydantic import BaseModel, Field, SecretStr, field_validator, model_validator class RepositoryConfig(BaseModel): """Configuration for a GitHub repository.""" url: str = Field(..., description="GitHub repository URL") branch: str | None = Field( None, description="Branch to track (defaults to default branch)" ) access_token: SecretStr | None = Field( None, description="GitHub access token for private repos" ) enable_domain_analysis: bool = Field( False, description="Enable domain analysis for this repository" ) @field_validator("url") @classmethod def validate_github_url(cls, v: str) -> str: """Validate GitHub URL format.""" # Allow local file paths for testing/integration scenarios if v.startswith("file://"): return v # Accept both HTTPS and SSH formats github_patterns = [ r"^https://github\.com/[\w-]+/[\w.-]+/?$", r"^git@github\.com:[\w-]+/[\w.-]+\.git$", r"^git@github\.com:[\w-]+/[\w.-]+$", ] if not any(re.match(pattern, v) for pattern in github_patterns): msg = f"Invalid GitHub URL: {v}" raise ValueError(msg) return v.rstrip("/") # Remove trailing slash if present class ScannerConfig(BaseModel): """Scanner configuration.""" sync_interval: int = Field( 300, ge=60, description="Repository sync interval in seconds" ) storage_path: Path = Field( Path("./repositories"), description="Local storage path for repositories" ) exclude_patterns: list[str] = Field( default_factory=lambda: [ "__pycache__", "*.pyc", ".git", ".venv", "venv", "env", "node_modules", ".pytest_cache", ".mypy_cache", ".ruff_cache", "*.egg-info", "dist", "build", ".coverage", "htmlcov", ], description="Patterns to exclude from scanning", ) max_file_size_mb: int = Field( 10, ge=1, le=100, description="Maximum file size to process in MB" ) use_git: bool = Field(True, description="Use Git for change tracking") git_branch: str = Field("main", description="Default Git branch") class ParserConfig(BaseModel): """Parser configuration.""" languages: list[str] = Field(["python"], description="Languages to parse") chunk_size: int = Field( 100, ge=10, le=1000, description="Chunk size for large files" ) max_depth: int = Field( 10, ge=1, le=50, description="Maximum depth for nested structures" ) extract_docstrings: bool = Field(True, description="Extract docstrings") extract_type_hints: bool = Field(True, description="Extract type hints") class EmbeddingsConfig(BaseModel): """Embeddings configuration.""" model: str = Field("text-embedding-ada-002", description="OpenAI embedding model") batch_size: int = Field( 100, ge=1, le=1000, description="Batch size for embedding generation" ) max_tokens: int = Field( 8000, ge=100, le=10000, description="Maximum tokens per chunk" ) use_cache: bool = Field(True, description="Cache embeddings locally") cache_dir: Path = Field(Path(".embeddings_cache"), description="Cache directory") generate_interpreted: bool = Field( True, description="Generate interpreted embeddings" ) class DatabaseConfig(BaseModel): """Database configuration.""" host: str = Field("localhost", description="Database host") port: int = Field(5432, ge=1, le=65535, description="Database port") database: str = Field("code_analysis", description="Database name") user: str = Field("codeanalyzer", description="Database user") password: SecretStr = Field(..., description="Database password") pool_size: int = Field(10, ge=1, description="Connection pool size") max_overflow: int = Field(20, ge=0, description="Maximum overflow connections") vector_dimension: int = Field(1536, description="Vector dimension for pgvector") index_lists: int = Field(100, description="IVFFlat index parameter") class MCPConfig(BaseModel): """MCP server configuration.""" host: str = Field("127.0.0.1", description="Server host") port: int = Field(8080, ge=1, le=65535, description="Server port") allow_origins: list[str] = Field(["*"], description="CORS allowed origins") rate_limit_enabled: bool = Field(False, description="Enable rate limiting") rate_limit_per_minute: int = Field(60, ge=1, description="Rate limit per minute") request_timeout: int = Field( 30, ge=1, le=300, description="Request timeout in seconds" ) class RankingWeights(BaseModel): """Query ranking weights.""" semantic_similarity: float = Field(0.6, ge=0.0, le=1.0) keyword_match: float = Field(0.2, ge=0.0, le=1.0) recency: float = Field(0.1, ge=0.0, le=1.0) popularity: float = Field(0.1, ge=0.0, le=1.0) @model_validator(mode="after") def validate_sum(self) -> "RankingWeights": """Validate that weights sum to 1.0.""" total = ( self.semantic_similarity + self.keyword_match + self.recency + self.popularity ) if abs(total - 1.0) > 1e-3: msg = f"Ranking weights must sum to 1.0, got {total}" raise ValueError(msg) return self class QueryConfig(BaseModel): """Query configuration.""" default_limit: int = Field( 10, ge=1, le=100, description="Default search result limit" ) max_limit: int = Field( 100, ge=1, le=1000, description="Maximum search result limit" ) similarity_threshold: float = Field( 0.7, ge=0.0, le=1.0, description="Similarity threshold" ) include_context: bool = Field(True, description="Include file context in results") context_lines: int = Field(3, ge=0, description="Number of context lines") ranking_weights: RankingWeights = Field( default_factory=lambda: RankingWeights( semantic_similarity=0.6, keyword_match=0.2, recency=0.1, popularity=0.1 ) ) class LoggingConfig(BaseModel): """Logging configuration.""" level: str = Field("INFO", pattern="^(DEBUG|INFO|WARNING|ERROR|CRITICAL)$") format: str = Field("json", pattern="^(json|text)$") file_enabled: bool = Field(True, description="Enable file logging") file_path: Path = Field(Path("logs/mcp-server.log"), description="Log file path") file_rotation: str = Field("daily", description="Log rotation schedule") file_retention_days: int = Field(7, ge=1, description="Log retention in days") console_enabled: bool = Field(True, description="Enable console logging") console_colorized: bool = Field(True, description="Colorize console output") class MonitoringConfig(BaseModel): """Monitoring configuration.""" metrics_enabled: bool = Field(False, description="Enable metrics collection") metrics_port: int = Field(9090, ge=1, le=65535, description="Metrics port") health_check_enabled: bool = Field(True, description="Enable health check endpoint") health_check_path: str = Field("/health", description="Health check path") profiling_enabled: bool = Field(False, description="Enable performance profiling") profiling_path: Path = Field(Path("profiles/"), description="Profiling data path")