"Using Supabase in Flutter applications" matching MCP servers:

Persistent memory and pattern learning for AI coding assistants via MCP.

Real TypeScript MCP server for connecting Zynx agents to Supabase through safe, allowlisted tools.

Experimental read-only MCP for Supabase application data: memory_search, memory_get and memory_list_recent under user-bound PostgreSQL RLS. Setup and demo: https://github.com/jryski/Supabase_user_MCP/blob/main/docs/GETTING_STARTED.md . Public preview is POSIX-only and synthetic-only; native Windows, hosted OAuth, one-click installation and writes are not available.

Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.

Enables AI tools to query context from a local JSON data source via stdio, demonstrating the Model Context Protocol.

Local-first MCP server that extracts structured knowledge from markdown notes into SQLite with full-text search, enabling AI coding tools to retrieve relevant context offline at zero cost.

MCP server for managing project memory in AGENTS.md. Allows agents to record and remove durable facts using save/forget tools.

MCP server providing persistent memory for AI applications, enabling Claude to store and recall memories with hybrid search and knowledge graph.

An intelligent memory MCP server that provides AI applications with semantic search, entity extraction, and knowledge graph capabilities using local Redis caching and optional cloud sync. It enables LLMs to store and retrieve long-term context across sessions with high-performance multi-tier storage.

MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.

Graph-based MCP server for persistent AI memory, session checkpointing, context compression, and cross-session context management for LLM applications.

Enables AI applications to add, search, update, and delete long-term memories using the Mem0 Memory API, allowing agents to persistently remember user preferences, conversation history, and contextual information across sessions.

Provides shared real-time context for Claude Code agents, including scope awareness, prior decisions, and anti-overlap, using only files in the repo.

A neurosymbolic AI server combining Prolog’s symbolic reasoning with Model Context Protocol (MCP) for hybrid AI applications.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.

Enables AI assistants and applications to interact with Airtable bases through a secure OAuth-authenticated MCP interface, providing complete API coverage for all Airtable operations.

Causal graph memory engine for AI agents. Scores memories using relevance × connectivity × reactivation, connects them in a causal graph, and actively forgets irrelevant ones. 11 MCP tools including store, recall, search, traverse, and explain.

Enterprise MCP server with memory management, API key control, and system health tools, ready for Claude integration with Supabase.

Persistent project memory for AI models and coding agents. Memory MCP stores architecture, decisions, tasks, warnings, preferences, and session state in Supabase so OpenCode, Claude Code CLI, Qwen Code, Codex, or any MCP-compatible client can resume work without losing context.