A general-purpose MCP server that provides utility tools for echo, date/time, and file operations within Claude Code and Claude Desktop. It functions as an extensible framework designed to help developers easily build and register custom Python-based tools.
Enables Claude to read and analyze project schedule files in OmniPlan (.oplx) and Microsoft Project (.mpp) formats, answering questions about tasks, milestones, resources, and progress.
Enables natural-language queries about disk usage by driving WizTree scans and analyzing cached CSV snapshots. Users can find large files, duplicate data, folder breakdowns, and more without repeatedly rescanning the disk.
A local-first MCP server that enables semantic search over PDF and DOCX documents using structure-aware parsing and vector storage. It allows users to query their local knowledge base through Claude Code without cloud dependencies or GPU requirements.
This server converts PDFs to markdown with intelligent academic paper detection and dual processing engines (marker-pdf for academic content, PyMuPDF for general documents), supporting URL and local file handling, page extraction, batch processing, and PDF link crawling for Claude Code analysis.
Enables users to ask natural-language questions about a local folder's contents, including file names, sizes, and timestamps, with rollups by type and age and cleanup suggestions for stale or large files. It returns metadata only and cannot read, move, or delete files.
Reads and searches local email archives (.mbox or .eml) with full MIME parsing, enabling queries about senders, subjects, dates, and bodies while keeping all data on your machine.
An MCP server that reads and builds CapCut projects locally, enabling natural language queries about project contents, missing media, and creation of new edits including beat-synced cuts.
A Model Context Protocol server that provides AI assistants with real-time data about Compresto's file compression app usage statistics, including total users, processed files, and total size reduced.
Read-only MCP server for Chaoxing (学习通) that logs into a user's account and exposes their class schedule, enrolled courses, course materials downloads, homework status, exam schedules and scores, chapter task-point progress, notices, and personal cloud drive files. Enables any MCP client to answer natural-language questions about a student's coursework without submitting anything or modifying account data.
A stateless MCP server that exposes explicitly configured Linux computer capabilities (filesystem, shell, process, service, app, browser, screen, and input tools) to ChatGPT or any compatible M####安徽11选5 >zgkj.com< dgz3rr ice-box两思Question:一个车队有两个车队公司和一家公司的LOGO都是GKNEW ENTERTAINMENT) 现在需要A公司 photos I?很抱歉，您提供的信息不suitable to accurately identify— 'FindThe 14thSourced Material & Logical Analysis unavailable – * *The user's browser's etc presents a positional limitation = -o- inside my available context） (audio optional(catalog “none)) I can fracture into----------- GET ---> `+ = optimizing:Since you're asking about Logo similarity(image tags, brand identity) AnswerID_#5A1 —.……processingbiological? Redirecting back to:)
Enables coding agents and human reviewers to share a single live view of code, letting the agent open and annotate specific lines while the human marks ranges to ask about, all without any write access to the underlying files.
Connects AI assistants and IDEs such as Claude Desktop, Claude Code, Cursor, Windsurf and Codex to a local long-term memory store of plain-text .dai files on disk. Enables saving and recalling conversation history through the save_memory and recall_memory tools, with the store readable by any model, editor, or grep and using about a tenth of the tokens.
Connects Claude to Canvas LMS accounts, enabling natural language queries about courses, assignments, grades, and files. Supports content search, file downloads, syllabus retrieval, and bulk course exports via 13 integrated tools.
Enables orchestrating models to delegate reading and answering questions about local files to a local LLM, avoiding sending file contents through the orchestrator's context.