"Managing Other MCP Servers" matching MCP servers:

Provides AI assistants with long-term semantic memory capabilities through local vector-based storage. Enables storing, recalling, and managing information across sessions with complete privacy using ChromaDB, with no data ever leaving your machine.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

Connect your Pinecone projects to Cursor, Claude, and other AI assistants

Enables agents to run semantic search across one or more local project directories by automatically maintaining a LAN-local Qdrant index with Ollama embeddings. Indexing, staleness checks, and incremental updates happen transparently, so users can query code by meaning without managing collections, chunks, or hashes.

An MCP server that enables saving, retrieving, and managing research content using ChromaDB vector storage and semantic search.

Enables MCP-capable coding agents to semantically search, store, and inspect a single local, cross-tool index of session histories and project docs. Agents can retrieve what other tools and prior sessions already discovered.

A Model Context Protocol server that allows Large Language Models to interact with Astra DB databases, providing tools for managing collections and records through natural language commands.

An MCP server for OpenServerless that exposes action tools for creating, invoking, and managing API endpoints with integrated services like S3, PostgreSQL, Redis, and Milvus.

An MCP server for querying and managing LlamaIndex documents stored in Qdrant vector databases, with automatic embedding model detection and extensive tools for search, retrieval, and collection management.

Provides persistent memory and knowledge graph capabilities for AI assistants using local SQLite storage. Enables creating, searching, and managing entities, relationships, and observations with vector search support across conversations.

Enables managing knowledge bases in Open WebUI and retrieving relevant fragments from Qdrant-stored knowledge through an MCP client.

Gives Claude Desktop, Cursor, Cline, and other MCP-compatible AI tools persistent memory, enabling them to store and recall information seamlessly across sessions.

Enables AI agents to access hybrid vector, reasoning-based tree retrieval, and agent memory through the Model Context Protocol (MCP), supporting Claude Desktop and other MCP-compatible clients.

An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.

Enables Claude Code, Cursor, and other agents to ingest and query shared memory through scoped, expiring capability tokens. Each read and refusal is checked in the database and recorded in an audit log.

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

Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.

RAGX MCP Server enables retrieval-augmented generation with document ingestion, hybrid search, and agentic answering using Claude, exposing tools for querying, searching, and managing documents.

Wraps n8n with an MCP server and vector storage to enable semantic search, management, and execution of automated workflows. It integrates with other tools to make workflows searchable and orchestratable within a larger automation ecosystem.

Exposes a shared graph-based context engine as MCP tools for Claude, Copilot, and other AI agents, enabling knowledge ingestion, recall, search, and LLM-ready context assembly across sessions.

Provides persistent cross-session memory for Claude and other AI agents, letting them store and automatically retrieve decisions, patterns, and knowledge across conversations, restarts, and context limits. It can be self-hosted on Redis or Valkey, or used through a managed SaaS.

An MCP server that provides long-term semantic memory via Garnet Vector Sets, enabling agents to store text embeddings and retrieve relevant context across sessions. It exposes tools for storing, recalling, and managing memories using pluggable embedding providers.

Self-hosted RAG MCP server that enables semantic and hybrid search over document corpora using local FAISS embeddings, with tools for indexing, retrieving chunks or full documents, uploading files, and managing multiple corpora via MCP.

Implements the Model Context Protocol for managing, ingesting, and querying structured and unstructured data with integration to graph databases, vector search, and LLMs.

Provides persistent cross-session memory for Claude and other AI agents by storing and automatically retrieving decisions, patterns, and knowledge across conversations and workspaces. It supports self-hosted Redis/Valkey or managed cloud deployments, helping preserve context beyond session restarts and context limits.

Enables any MCP-capable LLM client to search self-hosted long-term memory over markdown and PDF documents, combining dense semantic vectors with BM25 keyword retrieval and optional cross-encoder reranking. Exposes a read-only tool surface for querying incidents, runbooks, and other knowledge-base content, while writes happen out-of-band through ingestion jobs or a token-gated internal API.

Enables Claude and other AI agents to store and retrieve persistent cross-session memories, so decisions, patterns, and knowledge survive context limits, restarts, and compaction. Supports self-hosted Redis/Valkey or managed cloud deployment with workspace organization and semantic search.

An MCP server designed to assist with generating, converting, and translating Milvus SDK code by retrieving relevant documentation and snippets. It supports PyMilvus code generation, ORM-to-client conversion, and cross-language translation between Python, Java, Go, and other supported languages.

Exposes a shared long-term memory store over the Model Context Protocol so that Trae, WorkBuddy, and any other MCP client can save, search, list, fetch, and forget memories in the same on-disk JSONL store used by the DSH plugin, with no need to run DSH. Supports keyword plus local-vector semantic retrieval, project/global scoping, and cross-process locking so records written by any client are immediately visible to the others.

An MCP server providing semantic memory storage and retrieval using vector embeddings powered by LanceDB and Google Gemini. It supports multi-tenant isolation and bucket-based organization for managing structured memories through natural language queries.

A local RAG-powered documentation search system that uses vector embeddings and Qdrant to enable semantic search across markdown, HTML, and other file formats. It provides an MCP interface for AI tools like Cursor to intelligently query and retrieve information from local knowledge bases.

AI-powered MCP server that enables Claude and other LLMs to interact directly with construction documents, drawings, and specifications through advanced RAG and hybrid search capabilities.

Enables ChatGPT and other MCP-capable clients to query, compare, cluster, and semantically search a million Korean synthetic personas using structured filters and statistical summaries.

An unofficial MCP server that provides semantic search capabilities for Hugging Face models and datasets, enabling Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.

Enables Claude and other AI agents to retain and retrieve decisions, patterns, and knowledge across sessions, surviving context limits, restarts, and compaction. It supports self-hosted Redis/Valkey or managed SaaS, with workspace-scoped memories and semantic search.

A semantic memory store for AI agents, enabling persistent memory across sessions via MCP tools with semantic search, scoped access, and local embeddings.

An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.

Enables MCP clients such as Cursor, Claude, and Windsurf to store and recall user preferences, behaviors, and other text-based information across conversations, using vector similarity search so conceptually related items are found even when exact keywords differ.

Enables Claude Code and other LLM agents to capture, consolidate, and retrieve memories from conversation history, building a local-first knowledge graph with semantic search and dream-state consolidation.