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    Enables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.
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    MIT
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    Enables Claude to interact with core AWS services like S3, EC2, RDS, and CloudWatch, along with a generic SDK wrapper for any AWS operation. It also supports cost monitoring and optional vector store capabilities for document ingestion and search.
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    The Unlicense
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    Enables AI assistants to interact with Meilisearch through a standardized interface, supporting index and document management, search capabilities, settings configuration, task monitoring, and experimental vector search.
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    Enables AI agents to interact with INFINI Easysearch (compatible with Elasticsearch/OpenSearch APIs) through 121 tools covering cluster management, index operations, document manipulation, search queries, snapshots, and monitoring.
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    MIT
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    Provides a plug-and-play persistent memory layer for MCP-compatible AI assistants, enabling them to store, retrieve, and delete memories across multiple databases simultaneously using semantic vector search.
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    33
    MIT
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    Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.
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    Apache 2.0
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    An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.
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    185
    MIT
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    A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.
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    8
    MIT
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    A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.
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    Apache 2.0
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    A fully local, self-hosted memory server for MCP clients (Claude Code, Cursor, etc.) that provides persistent memory storage with semantic search, using local embeddings and a local Qdrant vector store.
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    MIT
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    A minimal RAG service that exposes a vector index for document retrieval via REST and MCP, allowing querying for relevant document chunks and returning a suggested LLM prompt.
    Last updated
    MIT