"An overview or information about file systems" matching MCP servers:

Enables developers and product owners to scan a codebase and extract a readable business overview—features, roles, access control, data model, and endpoints—along with findings on missing or inconsistent rules, and generate shareable HTML or Markdown reports and diagrams.

An MCP server that allows users to run and visualize systems models using the lethain:systems library, including capabilities to run model specifications and load systems documentation into the context window.

Automates SEO report generation from Google Drive data via Claude Desktop, supporting multiple data types and exporting reports in DOCX, HTML, PPTX, and PNG formats.

Provides systems thinking models as composable analysis lenses for architecture, infrastructure, DevOps, incident analysis, and technical decision-making.

Runs a local, file-based toolkit of 100 systems engineering skills that lets an AI assistant clean and map requirements, review specifications against ECSS, derive sub-requirements, check interfaces and budgets, and build verification and compliance matrices. It works on documents in a user-chosen local folder without sending customer data to a remote service or requiring an account or API key.

Enables file extraction, organization, and GitHub repository management through MCP tools, allowing AI agents to copy/move files, delete repositories, and automate workflows.

Local-first filesystem MCP bridge with ripgrep search, atomic writes, multiple transports

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 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.

Windows-first MCP server for deterministic file writes with atomic create, replace, and structured edit operations, preserving encoding and newline styles.

Enables AI models to perform file system operations (reading, creating, and listing files) on a local file system through a standardized Model Context Protocol interface.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

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.

An MCP server that gives AI agents file system access: read, write, search, hash, directory trees — 12 tools, zero dependencies, pure Python stdlib.

Provides AI agents with document conversion to Markdown, intelligently routing small and large files, and enabling targeted extraction via sections, search, and token budgets.

Enables Codex to locate QQ groups by name or number and search group file directories and recent chat attachments by filename, then download selected files through a locally deployed NapCat instance, returning paths, sizes, and SHA-256 hashes.

MCP server for converting between everyday file formats (CSV, Excel, Markdown, Word), including a GxP-compliant Markdown to Word template. Exposes four conversion tools over stdio.

Educational MCP server for basic file operations (read/write) via the Model Context Protocol.

An MCP server for managing tickets and meetings with CRUD operations, backed by PostgreSQL, and includes a REST API and React frontend.

Search local file contents recursively with keyword matching