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

  • A
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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
  • A
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    Enables semantic search and document management using a local Qdrant vector database with OpenAI embeddings. Supports natural language queries, metadata filtering, and collection management for AI-powered document retrieval.
    164
    36
    MIT
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    quality
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    A Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.
    4
    69
    4
    MIT
  • A
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    In-memory vector store with TF-IDF vectorization and cosine similarity search, paid per call via x402 micropayments.
    MIT
  • F
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    Enables AI agents to semantically search GitHub repository documentation by automatically fetching, vectorizing, and indexing content into an Upstash Vector database. It provides a standard MCP interface for agents to retrieve relevant documentation snippets through natural language queries.
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    A Model Context Protocol server that provides semantic understanding of codebases using Qdrant vector database, enabling AI assistants to search files by purpose, discover relationships between files, analyze architecture, and identify refactoring opportunities.
  • A
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    Read-only MySQL/MariaDB MCP server for running SELECT queries safely, with automatic read-only enforcement and query limits.
    3
    16
    MIT
  • A
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    quality
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    Provides local vector-based semantic memory storage for AI assistants to persist context and decisions across sessions using local embeddings and LanceDB. It enables private semantic search and session handoff capabilities to maintain long-term project context.
    252
    5
    MIT
  • A
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    An extensible MCP server for database operations that supports PostgreSQL for managing schemas, tables, data, and user permissions. It features automatic migration recording for DDL changes and integrates with various AI-powered editors like Cursor, Zed, and Claude Code.
    22
    2
    MIT
  • A
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    Enables AI assistants to interact with and manage multiple database types (PostgreSQL, MySQL, SQLite, SQL Server, MongoDB, Redis) through natural language, supporting query analysis, schema management, data analysis, backup/restore, and security analysis.
    6
    2
    MIT
  • A
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    quality
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    Provides a natural language interface for querying and managing PostgreSQL, MySQL, MariaDB, MSSQL, and SQLite databases using the Model Context Protocol. Users can explore database schemas and visualize query results through an integrated web dashboard.
    22
    MIT
  • A
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    quality
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    Enables AI assistants to interact with PostgreSQL and Supabase databases through natural language. Supports secure database operations including queries, migrations, and schema management with user-provided credentials.
    22
    1
    MIT
  • A
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    quality
    A
    maintenance
    Read-only Text-to-SQL MCP server for PostgreSQL and MySQL that lets users query databases using natural language, with robust multi-layer safety guarantees against writes.
    22
    MIT
  • A
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    Provides universal database operations for AI assistants through MCP, supporting 40+ databases including PostgreSQL, MySQL, MongoDB, Redis, and SQLite with built-in introspection tools for schema exploration.
    29
    MIT
  • A
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    quality
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    maintenance
    Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.
    3
    Apache 2.0