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"Using Qdrant vector database for code indexing" matching MCP servers:

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    Enables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible clients like Claude Desktop.
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    Turn SEC EDGAR filings into a searchable vector database, enabling natural language queries over company filings through Claude Desktop.
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    A shared, persistent MCP memory server for coding agents that enables storing and retrieving project decisions and context across different tools like Claude Code, Codex, and Cursor using semantic vector search.
    8
    MIT
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    MCP server that allows Claude AI to interact directly with MySQL databases, enabling query execution and table information retrieval through natural language.
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    4
    MIT
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    Provides semantic search capabilities using Qdrant vector database with multiple embedding providers, including hybrid search, code indexing, and git history search. Adds optional time-based recency scoring to search results.
    164
    MIT
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    MCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.
    MIT
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    A Model Context Protocol server for agents to search, store, ingest, and maintain data in a Qdrant vector database.
    Apache 2.0
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    MCP server for document ingestion and semantic search on Qdrant. Enables ingesting local documents, generating embeddings with OpenAI, and performing vector search with metadata filters.
    Apache 2.0
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    A unified Docker container that runs Qdrant vector database and provides REST API and MCP interfaces for vector storage and semantic search, compatible with Claude vector hooks.
    2
    MIT
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    Provides semantic memory capabilities using Qdrant vector database with configurable embedding providers, allowing storage and retrieval of information using vector similarity.
    2
    Apache 2.0
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    Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.
    2
    MIT