A tool for retrieving Discord messages and storing them in a vector database for semantic search and analysis.

MCP server for Zvec vector database

MCP server with FAISS local vector database for RAG

MCP server for Milvus vector database with session-aware connection management.

This is the workflow and scripts to help you build a Milvus vector database with search logics.

MCP server for SynapCores, the AI-native database: SQL, vector search, graph and agent memory for Claude, Cursor and any MCP client

A high-performance MCP (Model Context Protocol) vector database implemented in Erlang/OTP, designed to take full advantage of the Actor model and OTP supervision trees for fault-tolerant, concurrent vector operations.

Codai Better Database - High-Performance Vector Memory System with HPKV-inspired architecture and MCP server

MCP server for eustore.dev — autonomous sovereign storage + vector database for AI agents. S3-compatible storage and semantic vector search, fully API-driven, no dashboard needed.

A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. Built with Qdrant vector database for efficient knowledge storage and retrieval.

MCP server for Recall vector database with background indexing - search and index your code repositories and documentation

A lightweight vector database implementation with Model Context Protocol (MCP) server support, designed for local LLM applications. PyVector works with minimal dependencies and provides fallback implementations when optional dependencies are not available.

Model Context Protocol (MCP) server implementation for semantic vector search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic vector database search capabilities. You can use Claude and Cline AI as well.

An intelligent research assistant that searches, analyzes, and writes academic papers using a Retrieval-Augmented Generation (RAG) pipeline. Built with LangGraph, FAISS vector database,MCP and Google Gemini.

A Model Context Protocol server for fetching and storing documentation in a vector database, enabling semantic search and retrieval to augment LLM capabilities with relevant documentation context.

MCP server exposing the full Qdrant vector database API as 58 tools in 6 opt-in toolsets: hybrid and text search, a search-quality advisor, snapshots, observability. Read-only guard, SSRF protection. PyPI, Docker, MIT.

MCP server for vector database operations - Pinecone, Weaviate, Qdrant, and ChromaDB

Rust graph-vector database: OpenCypher (99.9% of evaluated TCK scenarios pass), vector search, graph algorithms, RESP + HTTP. LDBC SNB Interactive and BI complete; 1B edges on one machine.

YouTube MCP Server is an AI-powered solution designed to revolutionize your YouTube experience. It empowers users to search for YouTube videos, retrieve detailed transcripts, and perform semantic searches over video content—all without relying on the official API. By integrating with a vector database, this server streamlines content discovery.

A context database for LLMs: structured storage and hybrid retrieval (BM25 + vectors, RRF-fused, token-budgeted) over SQLite, exposed to Claude as an MCP server

MCP servers Collection MCP servers (Database, File system, API ,Vector databases and NOSQL ) Servers

Persistent, progressive agent memory for AI agents — encrypted hybrid vector+text knowledge database with an MCP server (recall, compile-context, knowledge CRUD, aikoql queries).

MCP Server for i-AUD MTSD document validation, generation, schema querying, control info extraction, database operations, and vector-free knowledge search (docs + script API catalog)

A local-first vector database with an extra dimension: intent. The same corpus and query return different results depending on what the asker is doing; built for agent memory

MCP server that wraps the Qdrant vector database API as tools.

MCP server for the Python Projects to provide context for large databases via semantic precise meaningful vector embeddings

An intelligent AI agent system that brings the world of Stardew Valley to life through conversational AI, RAG vector databases, and multimodal analysis.

Enterprise-grade AI agent orchestration with WASM-powered ReasoningBank memory and AgentDB vector database (always uses latest agentic-flow)

Define AI tools in YAML with natural language schemas. All tool usage is automatically stored in Qdrant vector database, enabling semantic search, filtering, and memory retrieval across sessions.

Git-native, reviewable knowledge modules for AI agents via MCP. No vector database required.

Extensible MCP server for semantic code search with plugin architecture supporting multiple embedding providers, vector databases, and data sources.

MCP server for eustore.dev — autonomous sovereign storage + vector database for AI agents. S3-compatible, semantic search, crypto-native.

This cli tool/Model Context Protocol (MCP) server chunks source code files using LangChain's RecursiveCharacterTextSplitter and stores the resulting chunks in a ChromaDB vector database, including line range metadata for each chunk.

Persistent memory for AI agents using git refs. MCP server + Claude Code skill. No vector database, no embeddings, no daemon — branches are scopes, push/fetch is sync.

An intelligent code memory system that leverages vector embeddings, structured databases, and knowledge graphs to store, retrieve, and analyze code patterns with semantic search capabilities, quality metrics, and relationship modeling. Designed to enhance programming workflows through contextual recall of best practices, algorithms, and solutions.

An LLM wiki your coding agent writes — agent memory you can open and read. The agent does the writing; easymem does the storage, full-text search and knowledge graph. No embedding model, no vector database.

this is a simple mcp server which does tool discovery in multiple mcp servers and puts in vector database so that llms are quickly be able to search available mcp tools as an aggregate capability and call them in dependent way, so it will increase the performance of the mcp system

Open-source Ask AI for your docs, with an MCP server for agents: cited answers on your own model key, from one static index built with your site. No vendor, no vector database. Starlight, Docusaurus and VitePress plugins, Next.js, or one script tag.

The ondocs Starlight plugin, under the name Starlight users search for: open-source Ask AI for your docs, with an MCP server for agents. Cited answers on your own model key, from one static index, with no vendor and no vector database.

Open-source Ask AI for your docs, with an MCP server for agents: cited answers on your own model key, from one static index built with your site. No vendor, no vector database. Starlight, Docusaurus and VitePress plugins, Next.js, or one script tag.

Enterprise-ready vector database toolkit for building searchable knowledge bases from multiple data sources. Supports multi-project management, automatic ingestion from Confluence/JIRA/Git, intelligent file conversion (PDF/Office/images), and semantic search. Includes MCP server for seamless AI assistant integration.

Local RAG server for Claude Code, Cursor and other MCP clients: hybrid BM25 + vector search with cross-encoder reranking on by default, 13 MCP tools, 33 file types including code, re-indexing on file changes. One Python process with embedded ChromaDB and FastEmbed ONNX: no Docker, database server or API key; offline after the first model download.

Knowledge management server

Vector MCP Server for AI Agents - Supports ChromaDB, Couchbase, MongoDB, Qdrant, and PGVector

High-performance, SIMD-accelerated vector search engine and MCP server for .NET 10. Optimized for LLM embeddings and semantic search.

A semantic search engine that transforms your documents into an intelligent, searchable knowledge base using vector embeddings and AI

Turn your Readwise library into a blazing-fast, self-hosted semantic search engine – complete with nightly syncs, vector search API, Prometheus metrics, and a streaming MCP server for LLM clients.

Shared, persistent vector memory over MCP — give Claude Code, Cursor, Codex, and other agents a common long-term memory backed by Qdrant.

Self-hosted MCP server giving AI coding agents a shared, persistent memory over your code and docs (Qdrant, Ollama, tree-sitter).

Contract Analysis Agent

LibSQL-based persistent memory tool for MCP

A Model Context Protocol (MCP) server that provides agentic search capabilities with support for vector search using Qdrant, full-text search using TiDB, or both combined.

Agent skills and the Helix MCP for Helix Foundry and HelixDB: company context and memory for agents, a cited knowledge graph of your data, and graph-vector queries

MCP server for persistent memory using Qdrant vector store

MCP server for RushDB, the database for AI: schema-first tools for Claude, Cursor and other MCP clients

Model Context Protocol server that brings Moorcheh search and RAG tools to MCP clients.

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An evidence-based advisor for RAG stack decisions — every claim backed by rag-retriever-bench measurements. MCP + CLI.

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MCP server for local vector search over portable RVF files: Claude Code, Cursor and other MCP clients create, fill and query HNSW-indexed .rvf stores over stdio or SSE.