"WhatsApp MCP (Model Context Protocol) Integration" matching MCP servers:

Summarizes Slack activity and GitLab merge requests with OpenAI to help generate daily standups, weekly reports, or quarterly check-ins.

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.

MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.

A personal MCP for WhatsApp, with voice-note and call transcripts. Search and summarize your chats, read what was said in voice notes and calls, and organize chats, labels, notes, reminders and scheduled messages. Runs locally with the Kaption extension for WhatsApp Web (Chrome, Edge) or the Kaption desktop app. No send-now tool. Setup: https://kaptionai.com/mcp/

The URL-Context-MCP MCP Server provides a tool to analyze and summarize the content of URLs using Google Gemini's URL Context capability via the Gemini API. Now also supports optional grounding with Google Search alongside URL Context. The server is designed to follow prompt-only orchestration: con

Compresses, deduplicates, and filters tool outputs to optimize token usage and context window for AI agents.

Provides a bulk_read_files tool that reads specified files and forwards them to a remote backend for summarization, so agents receive concise summaries instead of raw file contents.

A simple notes MCP server that enables creating, listing, and summarizing text notes via resources, tools, and prompts.

Enables AI assistants to save, load, and search conversation context with AI-powered summarization and auto-tagging. Demonstrates semantic intent patterns and hexagonal architecture for maintainable AI-assisted development.

An MCP (Model Context Protocol) server specialized in deciphering PDF internal structures.

This TypeScript-based MCP server allows users to manage and interact with a note system through Model Context Protocol, enabling note creation and summarization with URIs and metadata.

A Model Context Protocol server that analyzes YouTube videos, enabling users to extract transcripts, generate summaries, and query video content using Gemini AI.

A server that provides document processing capabilities using the Model Context Protocol, allowing conversion of documents to markdown, extraction of tables, and processing of document images.

This TypeScript-based MCP server allows users to manage a simple notes system through creating and summarizing text notes using Model Context Protocol (MCP).

Enables local tool calling over Model Context Protocol via stdio, providing deterministic tools such as calc.add, text.word_count, and text.summarize_naive after JSON-RPC handshake and discovery.

Enables AI assistants such as Claude, ChatGPT, and Cursor to query, summarize, manage, and schedule WhatsApp conversations and related contacts, groups, labels, media, reminders, and analytics through the Model Context Protocol while processing remains local in the browser extension.

Provides detailed character analysis for text, counting total characters, characters without spaces, letters, numbers, and symbols through a simple MCP tool interface.

A Python-based MCP server adapted from the n8n_agent project that implements a note storage and summarization system. It enables users to create, retrieve, and summarize notes through the Model Context Protocol.

Enables AI agents and MCP clients to compress retrieved web documents against a query, pruning irrelevant noise through deterministic algorithmic scoring so downstream context stays lean. It exposes this capability over the Model Context Protocol for integration with Claude Desktop, Cursor, and similar clients.