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    Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.
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    Provides unified access to 100+ AI models from OpenAI, Anthropic, Google, AWS Bedrock and other providers through a single MCP interface. Enables seamless switching between different AI models using LiteLM's unified API without requiring separate integrations for each provider.
    3
    9
    MIT
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    An MCP server with RAG capabilities that provides AI clients secure access to local notes, documents, web pages, and task management tools for developer operational tasks. Features file operations, web scraping with content cleaning, and personal knowledge corpus search functionality.
    7
    MIT
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    An MCP server that retrieves and provides AI model information from OpenAI, Anthropic, and Google through a unified interface.
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    MIT
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    A meta-MCP server that uses Llama AI as an orchestrator to intelligently route requests and coordinate workflows across multiple MCP services like Stripe, GitHub, and databases. Enables complex multi-service operations with AI-driven decision making and parallel execution capabilities.
    MIT
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    An unofficial MCP server that provides semantic search capabilities for Hugging Face models and datasets, enabling Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.
    20
    MIT
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    A self-hosted server providing shared memory, RAG document search, project maps, and role-based prompts for all AI agents via MCP and REST, enabling persistent context across devices and tools.
    2
    MIT
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    An extensible MCP hub that exposes internal services (chat, observability, RAG) as namespaced tools via FastMCP, with OpenAPI auto-generation, auth, and resilient error handling.
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    A smart memory system for AI assistants that retrieves relevant memories using an entity graph, semantic tree, and focus buffer, with context budgeting.
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    A central knowledge hub that connects AI tools to a unified 'Master Brain' with automated memory capabilities. It utilizes intelligent analysis to automatically capture and store key decisions, fixes, and learnings across various development environments.