"Parsing and Unparsing CSV Files for AI Context Use" matching MCP servers:

A stdio MCP server for safely reading, analyzing, validating, and transforming CSV/TSV files, with sandboxed file system access and configurable parsing options.

Enables Claude Cowork on Windows to read, write, edit, list, find, and regex-search files inside a WSL distribution over stdio, accepting Windows and Linux path spellings. Access is confined to server-fixed roots, with secrets files refused, key-shaped strings redacted, and no delete or move operations.

Enables AI assistants to analyze CSV files by providing statistical analysis and structural summaries via MCP tools.

Comprehensive CSV processing MCP server with 40+ operations for data manipulation, analysis, and validation. Features auto-save, undo/redo, and handles GB+ files

MCP server that bridges Ableton Live with AI models, enabling real-time project inspection and control such as track overview, device parameters, and audio analysis.

Generates test files locally (images, PDFs, CSVs, corrupted files, etc.) from natural language prompts, with configurable sizes and safety limits.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

Enables MCP clients to query project-scoped Graphify graphs, search, read, and write Obsidian Markdown notes, and recall or store persistent ICM memories over authenticated private HTTPS. It serializes access per project and supports slim and standard Docker deployments without embeddings or extra memory services.

Loads CSV files into a temporary SQLite database and provides comprehensive data analysis tools via MCP, enabling AI assistants to query, analyze, and export data using natural language.

A lightweight MCP server for basic file operations, enabling reading, writing, and listing files securely via the Model Context Protocol.

An MCP server that lets an AI agent use a real terminal the way a person does: type, press keys, click, read the screen, take a screenshot.

Control a real iPhone or iOS Simulator through a macOS daemon and XCTest runner over USB or Wi-Fi: read screen text, tap, type, swipe, inspect logs and replay flows with explicit outcomes. Browser sandbox: use readonly to inspect 12 preloaded flows without a phone. Reuse an existing sandbox from MCP Deployments, or delete it before starting another. Device control needs your Mac and iPhone.

Enables agents to interact with CLI applications like vim, htop, and gdb by sending key inputs and capturing terminal output through tmux sessions.

Enables Claude to see and control a real Android phone via MCP, reading the accessibility tree to tap, type, navigate, launch apps, and diagnose connectivity over ADB or cellular.

Enables coding agents to observe and control an authorized Android phone over ADB through MCP tools and a JSON CLI. It supports UI inspection, screenshots, taps, swipes, typing, key presses, app listing, and launching.

Manages .ai/ workspace folder, syncs assets with a cloud service, and generates environment-specific bootstrap files for AI context management.

Multi-account X (Twitter) automation that drives a real Chrome session with your own cookies, so no X API key is needed. 32 tools for posting, replies, search, engagement, and media, with draft approval on by default.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.