"Learning NestJS Framework Knowledge" matching MCP servers:

MCP server for managing notes with CRUD tools. It uses the same notes service as the GraphQL API, enabling natural language interaction with notes.

A robust server-side application that implements Model Context Protocol (MCP) for file operations, providing authentication and modular REST APIs for managing files, users, and posts.

AI learning assistant for families. Manages game time rewards based on school grades, homework, and bonus tasks. Syncs with EduPage/PRONOTE. 78 MCP tools with instruction workflows.

Enables AI assistants like Claude to interact with MongoDB databases through standardized MCP tools. Supports full CRUD operations, aggregations, and natural language queries with Clean Architecture and dual transport modes (STDIO/HTTP).

A comprehensive framework for building remote MCP servers on Cloudflare Workers featuring OAuth 2.1 authentication, PostgreSQL integration, and Stripe-powered paid tools. It enables developers to create scalable, platform-agnostic MCP tools with support for user registration and subscription-based functionality.

An MCP server that lets LLM agents accumulate organizational knowledge across sessions, scoped by domain, backed by Neo4j with enforced structural invariants.

A Cloudflare Workers-based service that enables intelligent searching, automatic code snippet recording, and statistical analysis of programming knowledge stored in Notion databases.

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

Connects to an embedded Kuzu graph database and provides read-only MCP tools for AI clients to query a knowledge graph.

An embedded knowledge graph in one SQLite file, exposing a MCP server for agents to read and write entities and relationships with full audit trail and time travel.

A persistent memory server that implements a local knowledge graph using the Kuzu embedded database to store entities, relationships, and observations. It enables AI models to maintain structured long-term context through searchable nodes and comprehensive tag-based organization.

Builds and manages a cardiology-focused knowledge graph in Neo4j by extracting medical entities and relationships from documents using LLMs. It enables users to ingest PDFs, refine extracted data, and perform natural language queries to gain clinical insights.

Enables governed, read-only SQL analytics, hybrid RAG knowledge search, financial metric calculation, and database schema introspection through MCP, with role-based access control, audit logging, and AI observability.

Enables semantic search and document management with support for text, PDF, and image uploads using your own Supabase database and OpenAI API keys. Supports multi-tenant deployment on Cloudflare Workers or local hosting.

A TypeScript MCP server for managing persistent knowledge graphs with entities, directional relations, and time-based observations. It enables users to create, search, and track structured information with built-in concurrency control and JSON file storage.

Enables LLM agents to accumulate and manage organizational knowledge across sessions in Neo4j, with enforced structural invariants and append-only semantics.

Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.

Enhances the MCP memory server by implementing PouchDB for robust document storage and enabling the creation and management of a knowledge graph that captures interactions via language models.

Enables LLMs to manage file-based knowledge bases with dual storage (Markdown + SQLite). Supports creating, searching, and organizing articles across multiple knowledge bases with full-text search capabilities.

MCP server for AI-powered roadside assistance case management, exposing SQLite-backed tools for querying case counts, statuses, and summaries through natural language via Gemini function calling.