"Information on PostgreSQL Database Management System" matching MCP servers:

Enables AI development tools to maintain context across chat sessions with automatic branching, progress tracking, and TODO management for different tasks.

Fireproof ledger database with multi-user sync

Local-first knowledge system for reasoning agents, exposing facts, evidence, documents, retrieval, and audit history through a thin stdio MCP server.

Cross-project memory for Claude Code, enabling local semantic recall and secure, git-versioned markdown storage of reusable knowledge across repositories.

Enables AI assistants to maintain persistent conversations and context between sessions through automated saving and global installation across projects. Provides zero-configuration memory persistence with automatic conversation history preservation.

Enables persistent storage and retrieval of decisions, settings, and operational rules across chat sessions, maintaining context continuity and decision consistency for long-term development projects through structured memory management.

This MCP server provides an AI-driven learning and knowledge management platform that automatically summarizes conversations, manages knowledge graphs, tracks project progress, explores new technologies, and generates study plans through specialized agents.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

MCP server for managing AI prompts with CRUD operations, categorization, and tagging. Enables users to store, organize, and retrieve their favorite prompts efficiently.

An MCP server that provides deterministic math computation (numeric, symbolic, unit, matrix) and hybrid retrieval over study notes/textbooks with citations, helping Claude become a reliable study partner.

Provides AI clients with a persistent, readable and writable long-term memory layer over MCP, enabling creation, retrieval, tagging, updating, and deletion of memories across sessions.

Enables real-time auditing of LLM long-term memory to detect contradictions, user-introduced falsehoods, staleness, and unsourced fabrications, while providing a safe memory store with tools like recall, review, and restore.

Enables MCP-capable AI clients to read and write structured, project-scoped memory through a Cloudflare Worker backed by D1 and R2.

A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.

On Board is a local MCP server that gives multiple AI agents and IDEs a shared project memory, ticket queue, and handoff history, so agents can seamlessly continue each other's work. It supports agent-to-agent wake events, enabling autonomous workflows like reject-fix-resubmit cycles without human relay.

Serves as a universal interface between AI Agents and AnalyticDB PostgreSQL databases, enabling metadata retrieval and SQL execution, with additional capabilities for knowledge graph and LLM memory management.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

A local MCP server that transforms an Obsidian vault into a structured learning interface through tools for concept extraction and gap analysis. It enables AI agents to generate study plans and align note-taking with implementation projects for a more cohesive learning workflow.

An MCP server that provides searchable local storage for Claude conversation history, featuring automatic topic extraction and weekly insight summaries. It enables Claude to retrieve context from past sessions through full-text search and organized file storage.

An automated system that processes raw text through a three-stage pipeline to assess data quality, enhance information, and generate structured knowledge graphs. It provides tools for triple extraction with confidence scoring and creates interactive HTML visualizations of the resulting graph.

Enterprise MCP server that exposes multiple MySQL databases to AI services through one endpoint, with API-key auth, per-key database scoping, a layered SQL guard, and a markdown schema-knowledge graph.

A local-first, open-source harness for organizing your files with an AI agent.

A Python-based system that provides AI-powered code reviews through simulated expert personas like Martin Fowler and Robert C. Martin, using the Model Context Protocol (MCP).

Tracks and manages code changes from Git repositories, generates documentation automatically, and provides AI agents with intelligent access to service documentation through MCP server integration with feature mapping and search capabilities.

Enables LLMs to read, search, and manage Obsidian vault markdown files, including YAML frontmatter, wikilinks, and graph operations through a secure stateless I/O layer.

Enables querying a Neo4j knowledge graph about Wroclaw University of Science and Technology using natural language. Converts user questions into Cypher queries and retrieves contextual information through an intelligent RAG pipeline with LLM-powered query routing.

Enables AI agents to create, manage, and track tasks within plans using Valkey as the persistence layer. Supports plan and task management with Markdown notes, status tracking, and prioritization through multiple transport protocols (SSE, Streamable HTTP, STDIO).

Enables persistent memory and semantic search for development workflows with hierarchical compression. Store and retrieve development knowledge across IDE sessions using natural language queries, circumventing context window limitations.

Enables searching, retrieving, and managing prompts for Claude Code through natural language, with tools for adding, updating, and deleting prompts.

Self-documenting MCP server enabling AI agents to autonomously create, manage, and query SQLite databases with enforced metadata requirements for discoverability.