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"Understanding SQL Server Schema and Querying" matching MCP servers:

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    An MCP server that provides persistent memory for database schemas by storing table structures and metadata in a local SQLite file. It enables LLMs to save, retrieve, and manage database snapshots to maintain a structured understanding of database architectures.
    5
    MIT
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    Enables AI agents to store and retrieve project context, bugs, decisions, and session logs by reading and appending markdown files in a local Obsidian vault, without requiring any cloud services.
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    MIT
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    Enables querying and retrieving content from Confluence through CQL searches and page content fetching, allowing Claude to seamlessly access information stored in Confluence workspaces.
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    MIT
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    Enables seamless integration between Claude and Dry smartspaces, turning conversations into persistent, structured data by creating memories and querying information.
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    MIT
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    TheWeave is a markdown-native memory architecture for Claude and any MCP-aware agent. Your agent's memory lives as plain .md files you own: a 5-verb MCP core over the vault, query-driven PageRank retrieval, bi-temporal facts (valid_from / valid_until), and a persona-as-vault model. No database and no embeddings server. The files are the memory, inspectable in your editor and versionable in git.
    2
    Apache 2.0
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    Enables AI assistants to interact with Neo4j graph databases through natural language, supporting Cypher queries, schema management, data manipulation, and graph algorithms.
    MIT
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    Enables interaction with Neo4j graph databases through Cypher queries, supporting both read and write operations, schema exploration, and remote database connections via SSE or STDIO transport protocols.
    5
    MIT
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    CoreModels MCP Server provides 16 tools for AI agents to manage graph-based data models, enabling creation, querying, and manipulation of entities, relations, and observations within structured knowledge graphs.
    1
    Apache 2.0
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    HTTP MCP gateway enabling remote agents to query a learning corpus combining Pinecone vector search and Neo4j graph database through tools like route_query, query_namespace, and graph_query.