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

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

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

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

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.

Gives AI agents a persistent vector memory by embedding text with Ollama and storing it in Qdrant, so they can save, batch-save, semantically search with payload filters, and delete memories across collections.

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.

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.

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

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.

Enables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.

Enables AI to perform fast, multi-source web searches and research through tools for web, social, news, GitHub, and document analysis.

Enables interaction with Qdrant vector database for storing, searching, and managing vectors with automatic text embedding.

An MCP server that provides long-term memory and semantic search using Qdrant and OpenAI embeddings, with tools for storing, searching, and managing knowledge.