"Managing Vercel Projects and Their Integrations" matching MCP servers:

Enables semantic memory storage and retrieval with natural language queries, deployed on Vercel with PostgreSQL pgvector and OpenAI embeddings.

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.

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

Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Enables natural-language semantic search of a project's code through a local vector index, so questions that plain grep cannot match return ranked file:line references. It also indexes or refreshes projects on demand, reports index status, and optionally flags near-identical code across files.

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.

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.

Semantic memory MCP server that gives AI agents a self-writing, priority-based memory with local semantic search and automatic contradiction handling. It persists across sessions and projects, entirely on your machine.

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.

Enables AI agents to interact with Milvus vector databases and Zilliz Cloud through natural language, allowing users to create clusters, manage collections, insert vector data, and perform semantic searches directly from their AI assistants.

Enables semantic code search across local projects and Git repositories using AI embeddings with ChromaDB. Supports both OpenAI and local Ollama models for private, enterprise-ready code analysis and similar code discovery.

A persistent local vector memory server that allows users to store and search project-specific context using LanceDB and local embeddings. It enables MCP-compliant editors to maintain long-term memory across different projects without requiring external API keys.

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

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.

Enables vector similarity search and serving of Svelte documentation via the MCP protocol, with support for local caching and multiple llms.txt documentation formats.

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 AI agents a local-first, persistent memory backed by a single SQLite file, letting them save decisions, preferences, and facts, then recall them by meaning or keyword across sessions and projects. Exposes tools for loading pinned context, searching, adding, updating, listing, and deleting memories, so agents stop forgetting between sessions.

Provides a plug-and-play persistent memory layer for MCP-compatible AI assistants, enabling them to store, retrieve, and delete memories across multiple databases simultaneously using semantic vector search.