"How to organize or locate my project file" matching MCP servers:

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

MCP server that converts PDF, video, web, and audio inputs into structured Markdown notes with support for checkpointing, batch processing, and Obsidian integration.

A note management MCP server that enables users to store, retrieve, and summarize personal notes via a custom URI scheme. It provides tools for adding content and prompts for generating brief or detailed summaries of stored information.

Enables semantic search over personal files using natural language, with optional AI summarization, all running locally.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Provides tools for text file analysis, including metrics like word counts and character frequencies, alongside file reading and directory browsing capabilities. This server enables LLMs to interact with and process local file content securely through the Model Context Protocol.

A local AI-powered file reader that connects a Python MCP server with Ollama's Mistral model for offline file summarization. It provides secure file discovery and reading capabilities without requiring API keys or cloud services.

Analyzes unstructured documents in a local folder, extracting structure and key terms, and supports generating summaries via a host LLM with validated, approval-based report saving.

MCP server for aggregating and analyzing hot news from multiple platforms, with AI-powered summaries, smart filtering, and multi-channel notifications.

A TypeScript-based MCP server that enables users to manage text notes and generate summaries, showcasing key MCP concepts like resource representation and LLM integration.

Enables managing documents on the filesystem through natural language, with tools for read, create, edit, delete, and commands for summarize, format, rewrite, and convert.

The Documentize MCP server exposes all document processing capabilities as tools for AI agents and LLM clients — convert, merge, extract, sign, and more, directly from Claude Desktop, VS Code Copilot, Cursor, or any MCP-compatible host.

This MCP server enables users to analyze YouTube videos by extracting transcripts and generating specialized content like summaries, chapter timestamps, and social media posts.

This MCP server enables AI assistants to perform calculations, fetch live weather, manage in-memory notes, access system info and note resources, and use reusable code review and summarization prompts.

tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.

Provides MCP-compatible AI clients with offline text analysis and rewriting tools, including statistics, extractive summaries, keywords, readability scores, case conversion, entity extraction, and diffing, all running locally without API keys or network calls.

Fetches, parses, and summarizes ETSI telecommunications and media standards documents, enabling Claude to autonomously look up and ingest standards mid-conversation for answering questions, generating conformant code, or writing XML.

Generates natural, fluent Chinese content that reads like human writing using the Qwen3-Next-80B-A3B model. Supports web search for latest information and direct file saving with customizable writing rules.

Enables delegating checkable subtasks such as summarization, search-and-summarize, and file downloads to a local OpenAI-compatible model, with mandatory result verification and job statistics.