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

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    IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real
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    JavaScript
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    Implements a secure message communication protocol for handling exchanges between the banking chatbot and Azure OpenAI, providing message queuing, reliability, and detailed logging.
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    Security-enforcing MCP proxy that sits between an AI agent and any number of downstream MCP servers, intercepting every tool call through a capability-token policy gateway that can allow, deny, or escalate to human approval before the call reaches any real tool. It also exposes built-in operator tools for approval workflows, audit trail queries, token management, voice/HUD output, and hierarchical
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    Apache 2.0
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    Enables querying WhatsApp OSINT API to gather intelligence on WhatsApp numbers, including business verification, user status, privacy settings, linked devices, and profile pictures.
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    MIT
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    A local MCP server that acts as a policy-based firewall for personal context, letting users control agent access to their data with deny-by-default rules, per-field grants, and an audit trail.
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    Apache 2.0
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    A security wrapper for MCP servers that provides trust-on-first-use pinning, guardrail scanning, and protection against prompt injection attacks. It acts as an intermediary layer to ensure universal compatibility and secure enforcement of server configurations across various MCP host applications.
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    Apache 2.0
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    A production-grade, type-safe MCP server for memory forensics via Volatility 3, enabling LLM-assisted incident response without timeouts or evidence spoliation.
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    MIT
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    Enables managing personal information with dynamic topic-based organization (tasks, meetings, contacts, etc.), supporting optional OTP authentication and AES-256 encryption for sensitive data with automatic backups.
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    Local MCP server for private, consent-based speaker identification and attribution, with per-language voiceprints and calibrated match thresholds. It lets AI assistants know who is speaking in a room or transcript without cloud services.
    MIT