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

MCP server that gives AI coding agents persistent, semantic memory via Qdrant vector search, enabling workspace-aware codebase, documentation, and decision search.

Enables semantic and hybrid search over documents and codebases backed by a Qdrant vector database, with collection management, document CRUD, metadata filtering, and AST-aware code indexing using local or cloud embedding providers. Supports natural language queries, incremental re-indexing of changed files, and custom configurable prompts for guided workflows.

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.

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.

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.

Gives multiple AI coding agents a shared, persistent memory by indexing your code and documentation into a vector database with AST-aware chunking and hybrid semantic/keyword search. Agents can search across projects and store, update, or forget decisions, conventions and lessons learned, all self-hosted with local embeddings.

Indexes codebases into Qdrant for semantic search, enabling AI assistants to find relevant code by meaning without re-exploring the repo.

Enables semantic search across multiple Qdrant vector database collections, supporting multi-query capability and providing semantically relevant document retrieval with configurable result counts.

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.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

Read-only MySQL/MariaDB MCP server for running SELECT queries safely, with automatic read-only enforcement and query limits.

Enables agents to optimize and clean SVG vector paths, rasterize SVGs into crisp sub-pixel PNGs at custom DPI with alpha preserved, generate high-error-correction vector QR codes for payment URIs, and compress images to WebP while stripping EXIF metadata. Tools are metered via x402 USDC micropayments on Base L2.

A SQL Server CLI and MCP server for Claude Code that supports standard SQL Server authentication for database interaction and management. It enables users to perform schema exploration, execute queries, and manage data records with built-in SQL injection prevention.

A database MCP server providing 33 tools across PostgreSQL, MySQL, and SQLite, with connection groups, rollback, dump/restore, and schema auto-discovery via natural language.

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.

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.

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.

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.

In-memory vector store with TF-IDF vectorization and cosine similarity search, paid per call via x402 micropayments.

Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.