"How to extract and analyze data from a MongoDB database" matching MCP servers:

Provides persistent memory for AI assistants using MongoDB Vector Search, enabling content storage, semantic search, and knowledge retrieval through MCP tools.

Enables small local models to persist and retrieve long-term user facts through remember and recall tools, using SQLite FTS5 keyword search for lightweight memory without external vector databases or heavy VRAM.

A production-grade MCP server that provides persistent long-term memory for AI agents using MongoDB, enabling them to store, search, update, delete, retrieve, and summarize structured project memories across developer workflows.

Fireproof ledger database with multi-user sync

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

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.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Provides a single-writer MCP server for a governance-grade knowledge base of markdown documents with version control and query capabilities.

Enables Claude and ChatGPT to share a single study-progress database for interview prep, coursework, and spaced repetition, with per-user data isolation and server-side SM-2 scheduling for due reviews and study plans.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

MCP server for MongoDB-powered memory bank management with multi-project isolation, version control, and real-time statistics.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A Model Context Protocol server that connects Cursor AI IDE with Obsidian notes and MongoDB, creating an integrated knowledge management system with real-time synchronization and search capabilities.

Provides AI clients with a governed, searchable catalog of go-to-market context, including ownership, measurement, lineage, runbooks, and bulk-change templates. Optionally integrates with the UTM Builder for governed UTM reference data, preview, and issuance.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

A persistent memory layer for AI tools that decouples personal data from AI's unstable memory, enabling you to mention information once and have it remembered forever across all conversations.

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.

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.