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Information on GraphRAG, AutoGen, AG2, Daydream, and HybridRAG

  • Why this server?

    This server is designed to guide tool usage in problem-solving, which can be relevant to setting up RAG or autogen workflows. It helps break down complex problems into manageable steps and provides recommendations for which MCP tools would be most effective at each stage, helpful in designing a complex system.

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    An adaptation of the MCP Sequential Thinking Server designed to guide tool usage in problem-solving. This server helps break down complex problems into manageable steps and provides recommendations for which MCP tools would be most effective at each stage.
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    125
    TypeScript
    MIT License
  • Why this server?

    Enables graph database interactions with Neo4j, allowing manipulation of graph data, which is relevant to GraphRAG.

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    An MCP server that enables graph database interactions with Neo4j, allowing users to access and manipulate graph data through natural language commands.
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    Python
  • Why this server?

    This facilitates knowledge graph representation with semantic search, using Qdrant for vector similarity - central to GraphRAG and HybridRAG.

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    Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.
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    • Linux
    • Apple
  • Why this server?

    A memory server for Claude that stores and retrieves knowledge graph data in DuckDB which can be relevant for GraphRAG implementations.

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    A memory server for Claude that stores and retrieves knowledge graph data in DuckDB, enhancing performance and query capabilities for conversations with persistent user information.
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    TypeScript
    MIT License
    • Apple
  • Why this server?

    This tool helps developers make better decisions through systematic, iterative refinement of thoughts and designs, it may help create better autogen flows.

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    Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code r
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    TypeScript
    MIT License
  • Why this server?

    Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes, useful if you want to create GraphRAG over your local files.

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    Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.
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    TypeScript
    • Apple
  • Why this server?

    Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context which can be useful for RAG.

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    An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. Uses Ollama or OpenAI to generate embeddings. Docker files included
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    MIT License
    • Apple
    • Linux
  • Why this server?

    Provides semantic search capability over Obsidian vaults and exposes recent notes as resources to Claude through the MCP protocol for GraphRAG implementation.

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    Provides semantic search capability over Obsidian vaults and exposes recent notes as resources to Claude through the MCP protocol.
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    Python
  • Why this server?

    Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking, which can be useful for GraphRAG and HybridRAG.

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    Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.
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    JavaScript
    MIT License
    • Apple
    • Linux
  • Why this server?

    Facilitates enhanced interaction with large language models (LLMs) by providing intelligent context management, tool integration, and multi-provider AI model coordination for efficient AI-driven workflows; this can be used for autogen workflows.

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    Facilitates enhanced interaction with large language models (LLMs) by providing intelligent context management, tool integration, and multi-provider AI model coordination for efficient AI-driven workflows.
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    Python