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

A local-only codebase indexer that extracts syntax-aware chunks, creates embeddings locally, and enables semantic code search and symbol lookup via MCP.

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.

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.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

A read-only MCP server providing curated Qdrant documentation for LLMs, enabling retrieval of the latest and most accurate documentation.

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.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

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.

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

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

Enables AI applications to run SQL queries and retrieve results from Oracle Database.