"Qdrant vector database and search engine" matching MCP servers:

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

An MCP server for semantic code search using Qdrant vector database, enabling natural language queries to find relevant code snippets across indexed codebases.

Turn SEC EDGAR filings into a searchable vector database, enabling natural language queries over company filings through Claude Desktop.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

MCP server that searches documents in Qdrant using embeddings from LMStudio. Takes a text query, converts it to a vector via LMStudio's OpenAI-compatible API, and performs semantic search in Qdrant.

Enables Claude to store and retrieve information with semantic search using Qdrant vector database, providing persistent memory for conversations, code, and documentation.

A CLI-first semantic code search tool with MCP integration for AI assistants, enabling semantic search, AST-aware parsing, and code analysis across 13 languages.

Combines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.

Enables AI assistants to interact with a Qdrant vector database by exposing collection, point, vector, payload, snapshot, search, recommendation, discovery, and observability operations as MCP tools.

Enables searching documentation from GitHub repositories and web pages via MCP tools, with in-memory indexing and caching for fast retrieval.

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.

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

MCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.

Enables AI assistants to interact with a Qdrant vector database, including embedding generation and vector search operations.

A Model Context Protocol server for agents to search, store, ingest, and maintain data in a Qdrant vector database.

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.

Integrates RAG into AI agents via MCP Server, supporting multiple vector database technologies for collection management and search operations.

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.

Enables accurate Quranic verse search through natural language queries, returning exact text with no hallucinations.

Provides semantic memory capabilities using Qdrant vector database with configurable embedding providers, allowing storage and retrieval of information using vector similarity.