MCP Server Neurolorap

Instructions

Implementation Reference

• src/mcp_server_neurolorap/server.py:40-74 (handler)

  The main handler function for the 'project_structure_reporter' tool. It creates a ProjectStructureReporter instance, analyzes the project, generates a markdown report, and returns the output path.
  async def project_structure_reporter( output_filename: str = "PROJECT_STRUCTURE_REPORT.md", ignore_patterns: list[str] | None = None, ) -> str: """Generate a report of project structure metrics.""" logger.debug("Tool call: project_structure_reporter") logger.debug( "Arguments: output_filename=%s, ignore_patterns=%s", output_filename, ignore_patterns, ) try: root_path = get_project_root() reporter = ProjectStructureReporter( root_dir=root_path, ignore_patterns=ignore_patterns, ) logger.info("Starting project structure analysis") report_data = reporter.analyze_project_structure() output_path = root_path / ".neurolora" / output_filename output_path.parent.mkdir(parents=True, exist_ok=True) reporter.generate_markdown_report(report_data, output_path) return f"Project structure report generated: {output_path}" except Exception as e: error_msg = f"Unexpected error generating report: {e}" logger.error(error_msg, exc_info=True) return f"Error generating report: {str(e)}" # Code collector tool
• src/mcp_server_neurolorap/server.py:118-121 (registration)

  Registration of the 'project_structure_reporter' tool using the MCP server's mcp.tool decorator.
  mcp.tool( name="project_structure_reporter", description="Generate a report of project structure metrics", )(project_structure_reporter)
• src/mcp_server_neurolorap/project_structure_reporter.py:17-39 (schema)

  TypedDict definitions for FileData and ReportData, providing type schemas for the analysis results used by the tool.
  class FileData(TypedDict): """Type definition for file analysis data.""" path: str size_bytes: int tokens: int lines: int is_large: bool is_complex: bool error: bool class ReportData(TypedDict): """Type definition for project analysis report.""" last_updated: str files: List[FileData] total_size: int total_lines: int total_tokens: int large_files: int error_files: int
• src/mcp_server_neurolorap/project_structure_reporter.py:41-411 (helper)

  The ProjectStructureReporter class containing all the core logic for analyzing project structure, counting lines/tokens, ignoring patterns, and generating the markdown report. Used by the handler.
  class ProjectStructureReporter: """Analyzes project structure and generates reports on file metrics.""" def __init__( self, root_dir: Path, ignore_patterns: Optional[List[str]] = None ): """Initialize reporter with root directory and ignore patterns. Args: root_dir: Root directory to analyze ignore_patterns: List of patterns to ignore (glob format) """ self.root_dir = root_dir self.large_file_threshold = 1024 * 1024 # 1MB self.large_lines_threshold = 300 # Load ignore patterns from .neuroloraignore and combine with provided # patterns self.ignore_patterns = self.load_ignore_patterns() if ignore_patterns: self.ignore_patterns.extend(ignore_patterns) def load_ignore_patterns(self) -> List[str]: """Load ignore patterns from .neuroloraignore file. Returns: List[str]: List of ignore patterns """ # Check for .neuroloraignore file ignore_file = self.root_dir / ".neuroloraignore" patterns: List[str] = [] try: if ignore_file.exists(): with open(ignore_file, "r", encoding="utf-8") as f: for line in f: line = line.strip() # Skip empty lines and comments if line and not line.startswith("#"): patterns.append(line) except ( FileNotFoundError, PermissionError, UnicodeDecodeError, IOError, ): pass return patterns def should_ignore(self, path: Path) -> bool: """Check if file/directory should be ignored based on patterns. Args: path: Path to check Returns: bool: True if path should be ignored """ try: relative_path = path.relative_to(self.root_dir) str_path = str(relative_path) # Check each ignore pattern for pattern in self.ignore_patterns: # Handle directory patterns (ending with /) if pattern.endswith("/"): if any( part == pattern[:-1] for part in relative_path.parts ): return True # Handle file patterns elif fnmatch.fnmatch(str_path, pattern) or fnmatch.fnmatch( path.name, pattern ): return True # Additional checks if "FULL_CODE_" in str(path): return True # Always ignore .neuroloraignore files if path.name == ".neuroloraignore": return True try: if ( path.exists() and path.stat().st_size > 1024 * 1024 ): # Skip files > 1MB return True except (FileNotFoundError, PermissionError): return True return False except ValueError: return True def count_lines(self, filepath: Path) -> int: """Count non-empty lines in file. Args: filepath: Path to file Returns: int: Number of non-empty lines """ try: # Try to detect if file is binary with open(filepath, "rb") as f: chunk = f.read(1024) if b"\0" in chunk: # Binary file detection return 0 # If not binary, count lines with filepath.open("r", encoding="utf-8") as f: return sum(1 for line in f if line.strip()) except (UnicodeDecodeError, OSError): return 0 def estimate_tokens(self, size_bytes: int) -> int: """Estimate number of tokens based on file size. Args: size_bytes: File size in bytes Returns: int: Estimated number of tokens (4 chars ≈ 1 token) """ return size_bytes // 4 def analyze_file(self, filepath: Path) -> FileData: """Analyze single file metrics. Args: filepath: Path to file Returns: dict: File metrics including size, lines, and tokens """ try: size_bytes = filepath.stat().st_size # Skip detailed analysis for large files if size_bytes > self.large_file_threshold: return { "path": str(filepath.relative_to(self.root_dir)), "size_bytes": size_bytes, "tokens": 0, "lines": 0, "is_large": True, "is_complex": False, "error": False, } lines = self.count_lines(filepath) tokens = self.estimate_tokens(size_bytes) return { "path": str(filepath.relative_to(self.root_dir)), "size_bytes": size_bytes, "tokens": tokens, "lines": lines, "is_large": False, "is_complex": lines > self.large_lines_threshold, "error": False, } except OSError: # Handle file access errors gracefully return { "path": str(filepath.relative_to(self.root_dir)), "size_bytes": 0, "tokens": 0, "lines": 0, "is_large": False, "is_complex": False, "error": True, } def analyze_project_structure(self) -> ReportData: """Analyze entire project structure. Returns: dict: Project metrics including all files and totals """ report_data: ReportData = { "last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "files": [], "total_size": 0, "total_lines": 0, "total_tokens": 0, "large_files": 0, "error_files": 0, } for dirpath, dirs, files in os.walk(self.root_dir): current_path = Path(dirpath) # Skip ignored directories dirs[:] = [ d for d in dirs if not self.should_ignore(current_path / d) ] for filename in files: filepath = current_path / filename if self.should_ignore(filepath): continue file_data = self.analyze_file(filepath) report_data["files"].append(file_data) report_data["total_size"] += file_data["size_bytes"] if file_data.get("error", False): report_data["error_files"] += 1 elif file_data["is_large"]: report_data["large_files"] += 1 else: report_data["total_lines"] += file_data["lines"] report_data["total_tokens"] += file_data["tokens"] return report_data def generate_markdown_report( self, report_data: ReportData, output_path: Path ) -> None: """Generate markdown report from analysis data. Args: report_data: Analysis results output_path: Where to save the report """ with output_path.open("w", encoding="utf-8") as f: # Header f.write("# Project Structure Report\n\n") f.write( "Description: Project structure analysis with metrics " "and recommendations\n" ) f.write(f"Generated: {report_data['last_updated']}\n\n") # Files section with tree structure f.write("## Project Tree\n\n") files = sorted(report_data["files"], key=lambda x: x["path"]) # Build tree structure current_path: List[str] = [] for file_data in files: parts = file_data["path"].split("/") # Find common prefix i = 0 while i < len(current_path) and i < len(parts) - 1: if current_path[i] != parts[i]: break i += 1 # Remove different parts while len(current_path) > i: current_path.pop() # Add new parts while i < len(parts) - 1: f.write("│ " * len(current_path)) f.write("├── " + parts[i] + "/\n") current_path.append(parts[i]) i += 1 # Write file entry f.write("│ " * len(current_path)) f.write("├── ") self._write_file_entry(f, file_data, tree_format=True) # Summary f.write("\n## Summary\n\n") total_kb = report_data["total_size"] / 1024 f.write("| Metric | Value |\n") f.write("|--------|-------|\n") f.write(f"| Total Size | {total_kb:.1f}KB |\n") f.write(f"| Total Lines | {report_data['total_lines']} |\n") f.write(f"| Total Tokens | ~{report_data['total_tokens']} |\n") f.write(f"| Large Files | {report_data['large_files']} |\n") if report_data["error_files"] > 0: f.write( f"| Files with Errors | {report_data['error_files']} |\n" ) # Notes f.write("\n## Notes\n\n") f.write("- 📦 File size indicators:\n") f.write(" - Files larger than 1MB are marked as large files\n") f.write( " - Size is shown in KB for files ≥ 1KB, " "bytes otherwise\n" ) f.write("- 📊 Code metrics:\n") f.write(" - 🔴 indicates files with more than 300 lines\n") f.write(" - Token count is estimated (4 chars ≈ 1 token)\n") f.write(" - Empty lines are excluded from line count\n") f.write("- ⚠️ Processing:\n") f.write( " - Binary files and files with encoding errors " "are skipped\n" ) f.write(" - Files matching ignore patterns are excluded\n\n") # Recommendations f.write("## Recommendations\n\n") f.write( "The following files might benefit from being split " "into smaller modules:\n\n" ) complex_files = [f for f in files if f["is_complex"]] if complex_files: for file_data in sorted( complex_files, key=lambda x: x["lines"], reverse=True ): lines = file_data["lines"] suggested_modules = self._calculate_suggested_modules( lines ) avg_lines = lines // suggested_modules f.write(f"- {file_data['path']} ({lines} lines) 🔴\n") f.write( f" - Consider splitting into {suggested_modules} " f"modules of ~{avg_lines} lines each\n" ) else: f.write( "No files currently exceed the recommended " "size limit (300 lines).\n" ) def _calculate_suggested_modules(self, lines: int) -> int: """Calculate suggested number of modules for splitting a file. Args: lines: Number of lines in the file Returns: int: Suggested number of modules """ return ( lines + self.large_lines_threshold - 1 ) // self.large_lines_threshold def _write_file_entry( self, f: Any, file_data: FileData, tree_format: bool = False ) -> None: """Write a single file entry in the report. Args: f: File handle to write to file_data: Data for the file entry """ size_kb = file_data["size_bytes"] / 1024 size_str = ( f"{size_kb:.1f}KB" if size_kb >= 1 else f"{file_data['size_bytes']}B" ) filename = file_data["path"].split("/")[-1] if file_data.get("error", False): f.write(f"{filename} (⚠️ Error accessing file)\n") elif file_data["is_large"]: f.write(f"{filename} ({size_str}) ⚠️ Large file\n") else: complexity_marker = "🔴" if file_data["is_complex"] else "" f.write( f"{filename} ({size_str}, ~{file_data['tokens']} tokens, " f"{file_data['lines']} lines) {complexity_marker}\n" )