"Understanding File Structures" matching MCP servers:

Analyzes a local codebase and exposes its structure, per-file summaries, dependency graph, and complexity hotspots as tools for MCP clients like Claude.

Stop pasting your file tree into Claude. Give any AI assistant real architectural understanding of a codebase — local, private, zero‑config.

Enables analysis of any GitHub repository to get architecture, file roles, execution flows, system design Q\&A, and structured agent context. Works with MCP-compatible clients like Claude Desktop, Cursor, and Windsurf.

A comprehensive bridge connecting AI agents to Unreal Engine 5.7 through 39 MCP tools for scene manipulation, asset management, and blueprint control. It enables real-time perception and editor automation using the Remote Control API and file-based protocols.

This MCP server enables AI models to analyze local Python codebases using abstract syntax trees, providing tools for file structure analysis, symbol search, import graphing, docstring auditing, and refactoring prompts without loading entire source files into context.

Enables MCP clients to scope and generate safe Odoo add-on starters locally, including drafting architecture plans, creating 11-file add-on scaffolds, validating them, and saving files to disk—all without API keys or network access.

A specialized architecture design MCP server that enables AI-assisted developers to plan optimal system structures through interactive consultations. It generates structured ARCHITECTURE.md and .ai-context.yaml files to guide AI coding tools in building scalable and cost-effective infrastructure.

Enables inspection and analysis of project structures to detect languages, frameworks, entry points, and dependencies across multiple programming languages including Node.js, Python, PHP, Go, Java, and Rust.

Connects Claude Desktop to BULC fire simulation software for AI-powered building design, enabling natural language creation of rooms, walls, and multi-story structures with spatial awareness and undo/redo support.

Enables AI agents to scaffold production-ready FastAPI applications from YAML architecture specifications, generating modular project structures with routers, models, and tests.

Enables AI agents to create, manipulate, and manage BPMN 2.0 diagrams programmatically, with support for Mermaid conversion, auto-layout, and file persistence.

Enables agents to analyze codebases (local or GitHub) and automatically generate Mermaid diagrams rendered as PNG images via Kroki, providing visual understanding of project structure and flow through file discovery, reading, and diagram generation.

An MCP server that enables users to generate structured XMind mind maps with hierarchical topics, notes, and labels through natural language. It features automatic file saving to the local Documents folder and can automatically open generated maps in the XMind application.

Builds a semantic graph of codebases across 38 languages and exposes 42 MCP tools for structured code understanding, search, and impact analysis.

Static codebase analysis as MCP tools — give AI coding agents a map of your repo instead of letting them burn half their tokens rediscovering it file by file.

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.

Validates and parses Draw.io XML syntax, enabling analysis and verification of diagram structures, especially for AI-generated content.

Analyzes C++ source code to automatically generate UML class diagrams in PlantUML format, extracting classes, inheritance relationships, and member visibility from directories or file contents.

Hosted stdio MCP for Cursor and Claude Desktop. Push markdown ADRs/docs; at a file-path fork, memory_for_file returns Decision / Rejected / Constraint / Citation. Google OAuth. Not a remote HTTP server.

Enables AI agents to query organizational architecture and governance constraints, returning evidence-grounded answers from documented structures.