"Create a ZIP file with Python code for an OSINT monitoring project" 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.

An AI tutoring system that runs as an MCP server, allowing Claude to access and interact with your local educational materials to provide personalized tutoring based on your actual course content.

A Python MCP server providing AI agents with tools for local knowledge base search, business context retrieval, and text summarization prompt generation.

Enables AI agents to search, watch, summarize, clip, and extract transcripts from YouTube videos, all without needing an API key or leaving the chat.

A simple note-taking MCP server that allows adding notes and summarizing them via a prompt.

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

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.

Enables Claude to read and analyze PDF documents with automatic OCR processing for scanned files. Features intelligent text extraction, caching for performance, and secure file access with search capabilities.

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.

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.

Fetches category data from a Q&A service API and returns a markdown summary grouped by category.

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

A Model Context Protocol server that implements a simple notes system, allowing users to create, list, and summarize text notes.

Enables LLM clients to search a firm's precedent vault and draft grounded contract clauses, legal memos, and document summaries.

A local-first Codex plugin that bundles an MCP server to safely interact with Mattermost, enabling channel/thread summarization, conversation search, and reviewed post publishing via the Mattermost REST API.

Enables Claude to read, rewrite, generate, and summarize LibreOffice Writer documents with preview-then-apply editing through MCP tools, authenticating via Claude Code login.

Integrates local language models (like Qwen3-8B) with MCP clients, providing tools for chat, code analysis, text generation, translation, and content summarization using your own hardware.

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.

Enables fetching, searching, and summarizing YouTube video transcripts with multi-language support.