"Reading Microsoft OneDrive files" matching MCP servers:

An MCP server that enables a private mobile-first reading experience in ChatGPT, managing multi-book shelves, reading progress, highlights, thoughts, and bookmarks, with secure cloud storage and AI co-reading of the current page.

An AI reading companion that curates next books based on the Chicago Plan curriculum and tracks user reading progress with personal tokens. It offers book recommendations, logging, coaching questions, and reading reports.

Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.

Standard coding agents waste 70% of context tokens re-reading raw files with shell grep. Synapse-MCP indexes your codebase into a sub-millisecond local AST graph—giving Claude Code, Cursor, and Windsurf exact caller trees, 60% lower token spend, and zero-hallucination refactoring.

Enables AI assistants to read, write, and manage Microsoft OneNote notebooks, sections, and pages through OAuth-authenticated MCP tools, resources, and prompts.

Exposes Microsoft BCQuality repository (Business Central development best practices) as structured tools for AI agents, enabling rule discovery, search, and application via MCP protocol.

Slimdex is a local MCP server that helps coding agents retrieve code narrowly—outlines, line ranges, symbol bodies, references, and intent-based searches—instead of reading whole files into context, featuring a persistent index and tools for exploration, editing, and session memory.

MCP server that gives Claude Code and other MCP clients persistent memory using plain Markdown notes stored on your disk and optionally synced to cloud storage (iCloud, OneDrive, Google Drive, Dropbox).

Enables AI agents to store and retrieve project context, bugs, decisions, and session logs by reading and appending markdown files in a local Obsidian vault, without requiring any cloud services.

Exposes a .mind/ folder to MCP agents, enabling reading files, searching memory, proposing changes, and listing directory contents.

Provides AI agents with Obsidian vault operations via the official CLI, including reading, writing, searching notes, and managing files, with security and concurrency protections.

Enables Claude to recall context from a merged knowledge graph of your Obsidian vault and code projects, providing tools to search, find neighbors, and discover paths before editing files. Works offline by reading a local brain.json.

A local MCP server that exposes personal data from JSON files via tools, resources, and prompts, enabling profile reading, project/experience lookup, data search, and bio generation.

A lightweight bridge server that connects WeChat Reading (WeRead) data with Claude Desktop, allowing seamless access to reading notes and book collections through natural language interactions.

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.

A local MCP server that gives Claude a durable reading room for EPUB and plain text books, enabling chunk-by-chunk navigation, annotations, progress tracking, and shared margin cards.

Enables Claude Code to query codebase knowledge graphs directly, reducing token usage 5x–71x by reading a compact graph.json instead of raw files.

Enables AI assistants to interact with Tencent Lexiang enterprise knowledge base for searching, reading, creating, and editing documents, managing files, and importing meeting recordings.

Provides LLM clients with persistent memory tools for searching, reading, creating, and updating notes, retrieving backlinks, listing recent notes, and managing tasks using self-hosted MySQL storage.

A Claude Code plugin that runs a cheap, always-on model as an MCP server to accumulate knowledge of your codebase, so your agent can ask broad questions instead of re-reading files.