"Accessing Your Supabase Projects Dashboard" matching MCP servers:

Crawls and indexes documentation websites to Supabase with vector embeddings for RAG, using smart sitemap discovery and Jina AI for fast content extraction with multi-project support.

Kollektiv

Enables semantic search and exact retrieval of banking document evidence from Supabase pgvector, with tools for BRD/RCA document filtering and previous RCA lookup.

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.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

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

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

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.

Project-scoped semantic workspace memory for AI coding assistants. Watches your project files and auto-indexes code and docs into Qdrant using tree-sitter semantic chunking and hybrid search (dense + sparse + RRF)

A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.

Shared, local memory for AI coding tools that stays true to your code. Claude Code, Cursor, and Codex share one memory per project; memories are linked to the files they depend on and retired automatically when those files change. Includes a local dashboard, import from CLAUDE.md/AGENTS.md, and export. Runs entirely on your machine.

Keyless local RAG over your own documents (ChromaDB) with traceable citations, exposed as MCP tools and a FastAPI endpoint.

An MCP server that streams K-line (candlestick) data from decentralized exchanges (DEXs) to power your AI agents and workflows.

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.

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.

Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.

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.

Headless geometric memory engine for AI agents — no Vector DB, no cloud, no API key. Store and retrieve by meaning using native Vector Symbolic Architecture (NVSA) math over O_DIRECT NVMe mapping. Runs entirely on your machine via MCP.

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.

Enables GitHub Copilot Chat to semantically search locally indexed source code, documentation, and project statistics from reference projects through a fully local vector store. This lets Copilot find existing implementations and conventions so it generates new code homogeneous with established patterns, without any API keys or data leaving the machine.

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.

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.

A self-hosted second brain MCP server that enables capturing thoughts with deduplication and semantic search using local embeddings and PostgreSQL with pgvector, all running on your own hardware.

Exposes LangChain and Anthropic Claude capabilities as tools for generating production-ready RAG systems, Supabase vector stores, and document ingestion pipelines. It enables users to instantly scaffold AI infrastructure and document processing code through natural language prompts in MCP-compatible clients.

Turns your Readwise library into a blazing-fast semantic search engine with a streaming MCP server for LLM clients, supporting vector search, nightly syncs, and deployment on serverless platforms like Vercel.

Context Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.

Shared memory and a knowledge graph for AI agents: hybrid vector and keyword search, RAG over your docs, scoped agent memory, and RDF/SPARQL and Cypher graph queries. Works without an embedding model; one server can serve many agents.

3D similarity dashboard for Qdrant + read-only MCP server (9 tools). Python stdlib only.

Gives multiple AI coding agents a shared, persistent memory by indexing your code and documentation into a vector database with AST-aware chunking and hybrid semantic/keyword search. Agents can search across projects and store, update, or forget decisions, conventions and lessons learned, all self-hosted with local embeddings.

Gives AI agents a durable, locally hosted temporal memory built from a markdown vault you own, exposing hybrid semantic/lexical/entity search over events, threads, transcripts, entity relationships with validity dates, and patterns with confidence decay. Writes are approval-gated, so agents can draft, commit, and sync new memories while keeping every index rebuildable and nothing leaving your machine.

Enables AI assistants to semantically search your entire local conversation history from Claude Desktop, ChatGPT and Claude Code, and to retrieve, browse, ingest and report on those conversations. All embeddings run locally, so no cloud, API keys, or data leave your machine.

Exposes private document collections as MCP tools so AI assistants can run hybrid dense-plus-BM25 retrieval with RRF fusion and optional reranking, returning ranked chunks with source citations. It also lets clients list collections, fetch document summaries, and inspect multimodal ingestion and query traces through a dashboard.

Local podcast transcription, embedding, and semantic search with an MCP server for AI assistant integration. All processing runs on your machine.

Provides a self-hosted knowledge index with document-level permissions, enabling AI agents to retrieve exactly the documents they are authorized to see via MCP. Supports OAuth 2.1, custom embedding models, and runs inside your network.

A pluggable and observable Retrieval-Augmented Generation framework that exposes hybrid search and document management tools via the Model Context Protocol. It features a complete ingestion pipeline with multi-modal support, automated evaluation using Ragas, and a Streamlit dashboard for real-time tracking.

A lightweight vector-based memory system for AI agents with tools to store, semantically search, and delete memories, using your own embedding model.