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

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    MCP server that gives AI agents read and write access to WhatsApp on macOS, reading local chat history offline and sending messages via pre-filled drafts with explicit user confirmation.
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    MIT
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    MCP server that pings 130+ free coding LLM models across 17 providers in real-time, ranks them by latency, and helps AI agents pick the fastest available model.
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    MIT
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    Manage WhatsApp Business templates and send messages from Claude, ChatGPT, Cursor, VS Code Copilot, or any MCP-compatible client — powered by the Meta Cloud API.
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    MIT
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    Enables Claude to interact with WhatsApp on macOS through the Model Context Protocol. It allows reading messages, searching contacts, listing chats, and sending replies via natural conversation.
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    MIT
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    An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.
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    MIT
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    Enables playing a Workato-themed text-based RPG through MCP tools, allowing users to explore dungeons, battle enemies, and manage character progression in an integration-themed adventure.
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    MIT
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    A comprehensive MCP server for managing AI-assisted Dungeons & Dragons campaigns, featuring tools for character sheets, combat tracking, and world-building. It enables players and DMs to interact with 5e game mechanics and query personal PDF rulebooks using RAG capabilities.
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    MIT
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    An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.
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    19,534
    19,824
    Elastic 2.0
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    Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.
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    MIT
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    Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.
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    Elastic 2.0
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    Provides official INDEC register designs and methodological rules to AI models, enabling accurate EPH data analysis code (R/Python) without hallucinations.
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