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Collaboration Between Multiple AIs via MCP (Master Control Program)

  • Why this server?

    This server uses a coordinated team of specialized AI agents, which directly addresses the 'two or more ai collaboration' aspect of the search.

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    An advanced MCP server that implements sophisticated sequential thinking using a coordinated team of specialized AI agents (Planner, Researcher, Analyzer, Critic, Synthesizer) to deeply analyze problems and provide high-quality, structured reasoning.
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    124
    Python
    • Linux
    • Apple
  • Why this server?

    This server enables communication and coordination between different LLM agents across multiple systems, which is directly related to the user's request for collaboration between AI agents.

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    Enables communication and coordination between different LLM agents across multiple systems, allowing specialized agents to collaborate on tasks, share context, and coordinate work through a unified platform.
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    TypeScript
    • Linux
    • Apple
  • Why this server?

    This server allows querying different language models and combining their responses, suggesting a form of AI collaboration via mcp.

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    A server that enables communication with multiple unichat-based MCP servers simultaneously, allowing users to query different language models and combine their responses for more comprehensive results.
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    JavaScript
  • Why this server?

    This server enables coordination of agents through shared finite state machines, demonstrating a specific type of structured AI collaboration via MCP.

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    An MCP server that enables coordination of agents through shared finite state machines (puzzles) where clients can create, monitor, and trigger state transitions of stateful resources.
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    9
    TypeScript
  • Why this server?

    This server optimizes context management for multiple AI client applications, implying potential collaborative use cases with context awareness.

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    An MCP server implementation that maximizes Gemini's 2M token context window with tools for efficient context management and caching across multiple AI client applications.
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    14
    TypeScript
  • Why this server?

    This server enables AI models to interact with multiple LLM providers, which could facilitate collaboration between different AI models.

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    Enables AI agents to interact with multiple LLM providers (OpenAI, Anthropic, Google, DeepSeek) through a standardized interface, making it easy to switch between models or use multiple models in the same application.
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    3
    Python
    MIT License
    • Linux
    • Apple
  • Why this server?

    This middleware server enables multiple isolated instances of MCP servers to coexist, indirectly supporting a broader collaborative environment by managing resources.

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    A middleware server that enables multiple isolated instances of the same MCP servers to coexist independently with unique namespaces and configurations.
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    17
    23
    TypeScript
    MIT License
  • Why this server?

    This server connects Gemini Pro to Claude Code, enabling the generation of detailed implementation plans and feedback on code changes, representing AI collaboration in a specific context.

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    An MCP server that connects Gemini 2.5 Pro to Claude Code, enabling users to generate detailed implementation plans based on their codebase and receive feedback on code changes.
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    3
    Python
    • Linux
    • Apple
  • Why this server?

    An MCP server that enables intelligent task delegation from advanced AI agents to more cost-effective LLMs, optimizing for cost while maintaining output quality.

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    An MCP-native server that enables intelligent task delegation from advanced AI agents like Claude to more cost-effective LLMs, optimizing for cost while maintaining output quality.
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    29
    Python
    MIT License
  • Why this server?

    Facilitates interaction and context sharing between AI models using the standardized Model Context Protocol, enabling collaboration across diverse AI systems.

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    Facilitates interaction and context sharing between AI models using the standardized Model Context Protocol (MCP) with features like interoperability, scalability, security, and flexibility across diverse AI systems.
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    Python