"How to get the file tree structure of a folder" matching MCP servers:

A runtime-free MCP server that converts source code into AST🌲, regardless of language.

Enables LLM agents to navigate source code structurally via tree-sitter over MCP, offering outline-based reading, AST pattern search, workspace-scale symbol indexing, call graph analysis, and built-in security audit queries.

An MCP server that indexes source code into a local database and provides tools for querying code symbols, dependencies, and tree structure for JavaScript/TypeScript, Java, and Python.

An MCP code-intelligence server for AI agents with pre-indexed AST cache, 62 MCP tools, and TOON-compressed output, enabling token-efficient code analysis and project health grading entirely locally.

A Model Context Protocol (MCP) server that provides code analysis capabilities using tree-sitter, designed to give Claude intelligent access to codebases with appropriate context management.

Enables fast semantic code search and analysis across 15+ programming languages. Supports searching for functions and classes, tracing code usage, detecting syntax errors, and analyzing code quality and structure.

Enables coding agents to inspect the syntax tree of a code snippet in over two dozen supported languages, listing one named node per line with field names and zero-based row-column ranges, so they can confirm node kinds, fields, and positions before writing queries or tree-walking code.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

Provides AI agents with powerful JavaScript/TypeScript code analysis and transformation capabilities using the tree-hugger-js library.

Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.

Enables AI agents to explore, analyze, and search file systems, with tools for directory trees, file statistics, duplicate detection, code counting, and multi-criteria file search.

Provides deep git file-level forensics tools to analyze detailed file histories, changes, and patterns within git repositories, focusing on individual file analysis rather than repository-wide operations.

Enables MCP clients to register projects and analyze source code through Tree-sitter WASM parsing, exposing tools for AST and symbol extraction, custom queries, reference finding, complexity and similarity analysis, dependency discovery, and text search. It also ships a web studio with a Monaco editor, AST explorer, Git view, and read-only SQLite console for interactive exploration.

An MCP server that provides a flexible lens into directory structures and files, enabling LLM clients to efficiently navigate and understand codebases with minimal noise. It offers secure, gitignore-aware file access with tools like directory listing, file reading, and grep-like search.

Enables precise file patching using SEARCH/REPLACE blocks, with mandatory directory sandboxing, automatic Python QA (Ruff/Black/MyPy), and git versioning for rollback.

Enables AI clients to analyze codebases through Tree-sitter (WASM) by exposing tools for AST and symbol inspection, structural queries, reference and dependency lookup, complexity and code-similarity analysis, and project registration with directory scanning. Ships alongside a web studio with a Monaco editor, AST explorer, and read-only SQLite console.

Enables checking project files like Dockerfile, .nvmrc, and CI workflows to determine whether pinned runtime, database, and OS versions are still supported, providing end-of-life dates, latest patches, and upgrade targets for 470+ products.

Enables intelligent handling of large files through smart chunking, search with regex support, line navigation, and streaming capabilities without loading entire files into memory.

Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.