"Vector Database" matching MCP servers:

A Model Context Protocol (MCP) server that provides intelligent search capabilities for discovering relevant Claude Agent Skills using vector embeddings and semantic similarity. This server implements the same progressive disclosure architecture that Anthropic describes in their Agent Skills enginee

Central registry for AI tools with vector search and knowledge graph, enabling intelligent tool discovery, chaining, and execution via MCP.

A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.

MCP Server for OceanBase database and its tools

An implementation for managing Neo4j graph database operations through the Model Context Protocol, enabling users to execute Cypher queries against their Neo4j database via AI assistants like Cursor and Claude Desktop.

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

An advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via Redis Stack, and temporary scratchpad storage. It also enables context compression through history rollups and facilitates inter-agent communication using a Redis-based Pub/Sub system.

Official Kimss MCP server — call your Kimss agents, models, files, and vector stores from Cursor and other MCP clients using your API key.

Enables LLMs to orchestrate AI agents and tasks, inspect DAG workflows, monitor cluster metrics, and perform safe database queries and statements through standardized MCP tools.

Enables real-time communication and shared vector memory between Claude Code, OpenAI Codex CLI, and Google Gemini CLI. It allows different AI platforms to exchange messages, share context, and collaborate through a unified MCP interface.

Enables MCP clients to discover and execute operational tools such as database mutations and system diagnostics over stdio, with deterministic structured outputs and Langfuse observability.

Connect your AI models to your favorite SurrealDB database, and let the LLMs do all your work for you.

Enables stateless human-in-the-loop DevOps automation, demonstrating MCP SEP-2322 multi-round trip requests for emergency database migration authorization.

A semantic router that provides unified access to multiple MCP servers through a single tool interface, using vector search to discover and execute tools across your entire MCP ecosystem while minimizing context token usage.

Enables agents to stamp events with vector clocks, resolve state via LWW CRDT reconciliation, and coordinate distributed swarms through token-bucket gossip, Raft leader/quorum voting, and two-phase commit. It runs over MCP with zero external dependencies, delivering deterministic happens-before causal ordering and convergence across agent networks.

A smart MCP gateway that routes AI agents to the right tools using hybrid search (vector + full-text) on MongoDB Atlas, collapsing the retrieval infrastructure into a single query.

A central registry MCP server that routes requests to specialized AI services including embeddings, PDF extraction, reranking, vector search (Qdrant), PostgreSQL, LLM completions, markup, and transcription. Includes a proxy server with RAG pipeline for document processing and retrieval.

MCP server that lets multiple coding-agent sessions on the same machine discover each other and collaborate through a shared SQLite database.

A multi-server implementation that uses OpenAI's LLM to orchestrate multiple tool servers providing web search, weather lookup, random facts, and PostgreSQL database querying capabilities.

Enables AI agents to share persistent memory via an MCP server using SQLite, supporting multi-tenant, categorized knowledge with TTL and semantic links, without requiring vector databases.

An MCP server that enables web search and document retrieval capabilities through Tavily API and LangConnect vector database, supporting AI agents in gathering information for comprehensive report generation.

Provides a set of MCP tools (file, database, GitHub, Slack, browser, calendar, email, vector search, Python execution) with safety constraints and OpenAI integration for enterprise architecture automation.

Provides a self-hosted shared work queue for AI agents via MCP, using a local SQLite database for task management.

A semantic proxy that reduces AI agent token usage by exposing only three core tools and using local vector embeddings to search for and execute hundreds of underlying MCP tools. It streamlines communication between agents and MCP Routers by identifying relevant tools through natural language queries.

Unified AI Agent SaaS Connector & Multi-Provider Gateway connecting to 1,000+ SaaS platforms, vector databases, and 500+ LLM models via MCP, REST API, and CLI.

A self-hostable, open-source, semantically-searchable Agent Skills registry delivered over MCP, with a three-tier progressive disclosure architecture.

A Meta-MCP Server that acts as a tool discovery service, helping AI assistants find appropriate MCP servers from a database of 800+ servers when they need capabilities that aren't currently available.

Self-hostable MCP server for portable AI agent playbooks: manage skills, MCP configurations, personas, instructions, memory, and canvas documents. The public Next.js source implements Streamable HTTP MCP at /api/mcp/manage; users can run it with the repository's Dockerfile and their own database configuration.

Enables multiple AI coding-agent sessions on one machine to hand tasks to each other and get results back through a shared local folder of JSON messages, with no cloud, database, or network service. Supports direct messages, broadcasts, blocking receive, and an optional headless worker that picks up, runs, and auto-replies to tasks.

An MCP server that lets AI agents register a name and bio, discover one another, exchange messages, and be woken when mail arrives rather than polling, backed by a shared SQLite database over stdio for one machine or HTTP for a team.