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    An MCP server that enables local AI models to receive guidance from remote 'senior' AI providers like OpenAI, Anthropic, and Gemini to solve programming problems. It features intelligent multi-turn dialogue management, context synchronization, and automated session history tracking.
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    An AI personality collaboration tool based on Model Context Protocol (MCP) that enables users to summon and collaborate with multiple AI personas for intelligent analysis and problem-solving.
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    MIT
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    缔零法则MCP是基于LLM和RAG技术搭建的实现完全替代人力的全自动化风险识别的内容安全审查平台,致力于通过代理AI技术减少人力成本,高效高精度为用户提供分钟级接入的内容风控解决方案,破解安全威胁,提供从风险感知到主动拦截策略执行的全链路闭环与一体化解决方案。This MCP tool is an AI-powered content security review platform built on LLM and RAG technologies, designed to achieve fully automated risk identification that completel
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    MIT
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    Don't let AI guess from screenshots. Give LLMs direct access to your React state, Context, and Data Grids. Features bidirectional communication via SharedWorkers & WebSockets. Docker gateway included.
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    Apache 2.0
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    Enables AI assistants to access and manage accounting data through the freee accounting API, supporting operations like transaction management, financial analysis, and account item management.
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    Mandatory human approval gate for autonomous AI agents. Before any critical, irreversible, or financially significant action executes — file deletion, production deployment, financial transaction — the agent calls oracle_validate. A human receives a real-time Telegram notification with full context and taps Approve or Reject. The agent waits for the decision before proceeding.
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    AI Agent Mission Control — 200+ MCP tools across 31 domains. Manage agents, experiments, workflows, crews, skills, tools, credentials, approvals, signals, budgets, marketplace, knowledge bases, chatbots, and more. Self-hosted, open-source (AGPL-3.0). Supports stdio + Streamable HTTP/SSE with OAuth 2.0 auth.
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    AGPL 3.0
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    Structured reasoning MCP server that decomposes problems into atomic steps (premise, reasoning, hypothesis, verification, conclusion) with confidence scoring, live visualization, and approval feedback.
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    MIT
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    Automatically enhances developer prompts with quality requirements, codebase context, and architectural patterns, then orchestrates other MCP servers to ensure AI coding assistants produce high-quality, structured code that follows best practices and security standards.
    Last updated
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    MIT