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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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    Enables MCP clients to offload sub-problems to fresh, isolated contexts via sampling, offering plan, think_aside, and run_plan tools for sequential reasoning without cluttering the main conversation with verbose intermediate steps.
    MIT
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    A Model Context Protocol (MCP) server implementation for the Google Gemini language model. This server allows Claude Desktop users to access the powerful reasoning capabilities of Gemini-2.0-flash-thinking-exp-01-21 model.
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    MIT
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    Facilitates two-stage reasoning processes using DeepSeek for detailed analysis and supports multiple response models such as Claude 3.5 Sonnet and OpenRouter, maintaining conversation context and enhancing AI-driven interactions.
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    MIT
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    Provides an MCP server that decomposes complex problems into ordered, isolated reasoning contexts, yielding compressed summaries before discarding intermediate steps. Enables sequential or parallel step execution with optional verification for more reliable LLM reasoning.
    1
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    Search and discover AI agents, skills, prompts, bundles and MCP connectors from a curated catalog of 4500+ assets. Provides tools for searching, browsing categories, and accessing detailed information about each asset.
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    MIT
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    Routes AI tasks to appropriate local LLM models (quick, coder, MoE, thinking) with automatic model selection, multi-backend support (Ollama, llama.cpp, Gemini), and parallel processing capabilities.
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    Provides AI assistants with real-time access to the AGNO framework documentation by enabling them to browse, search, and fetch documentation pages. This server allows users to query information about AGNO's Agents, Teams, and Workflows directly through MCP-compatible clients.
    16
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    Enables real-time tracking of AI task starts and completions with integrated webhook notifications for monitoring activity. It allows users to log progress and send task-related event data to external services via configurable webhook URLs.
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    MIT
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    Provides LLMs with cognitive tools (Thinking Models), memory structures (Cells), and multi-agent patterns (Organs) for advanced reasoning, memory management, and orchestration. Automatically routes requests to appropriate strategies and generates custom agent blueprints.
    12
    MIT
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    Multi-agent orchestration server that enables parallel task delegation, sequential pipelines, cron scheduling, and cross-model peer review via CLI providers like Codex, Antigravity, OpenCode, and Claude Code.
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    MIT
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    Enables AI-powered web searches using Google's Gemini 2.5 models with Google Search Grounding. Supports multiple Gemini models (Flash, Flash-Lite, Pro) with configurable thinking budget and web search capabilities.
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    MIT
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    A Model Context Protocol server that enables Claude Desktop and other MCP-compatible clients to leverage Google's Gemini AI models with features like thinking models, Google Search grounding, JSON mode, and vision support.
    6
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    MIT
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    The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.
    4
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    MIT