"Guidelines for Managing File Structures" matching MCP servers:

A TypeScript ESM MCP server exposing 20 public instruction tools and 7 utility tools, backed by 102 internal skills across 18 domain families — from requirements discovery and code quality through governance, resilience, and physics-inspired analysis.

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

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.

MCP server that gives Claude Desktop and other desktop MCP clients filesystem powers—read, write, edit, and manage files like AI coding assistants.

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 saving and managing files in a sandboxed local folder via MCP, with tools for listing, reading, writing, appending, searching, moving, and deleting files and directories.

Enables comprehensive file operations including reading, writing, searching, and editing files with advanced features like regex-based replacements, line-specific modifications, and directory-wide search capabilities. Provides 8 robust tools for safe file manipulation with content verification and detailed error handling.

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

A powerful, security-hardened MCP server for intelligent file organization. Provides high-level atomic tools for organizing files into categories, detecting duplicates, extracting EXIF/ID3 metadata, scheduling automatic organization, and more — for use with Claude, Cursor, and Gemini CLI.

A Model Context Protocol server for secure, sandboxed file operations and project scaffolding with built-in search and regex capabilities. It is optimized for Windows environments and features configurable access controls to restrict operations to specific directories.

Local-first MCP server that transforms civil case PDFs into queryable cases with structural provenance, evidence IDs, and page-level verification for drafting and factual review.

A Model Context Protocol server that enables enhanced file system operations including reading, writing, copying, moving files with streaming capabilities, directory management, file watching, and change tracking.

Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.

An MCP server that allows Claude and other LLMs to manage persistent memories across conversations through text file storage, enabling commands to add, search, delete and list memory entries.

Enables file operations such as counting, listing, compressing images, creating/extracting archives, copying/moving files, and merging/splitting PDFs via natural language.

Enables local ChatGPT/OpenAI MCP clients to read files and search within explicitly authorized directories using read-only, policy-constrained tools.

Enables uploading files to MinIO (S3 compatible) via MCP tools. Supports Base64 file upload and optional curl command generation.

Enables agents to securely stage, validate, and submit binary data for on-premises storage, then list, inspect, process via registered server-side processors, and token-guarded delete those assets using opaque identifiers. Upload bytes travel over a separate data plane rather than through MCP JSON, with staging, validation, and prepare/apply/verify flows enforced throughout.

This server provides an API to query Large Language Models using context from local files, supporting various models and file types for context-aware responses.