Mock everything your AI app talks to — LLM APIs, MCP, A2A, vector DBs, search. One package, one port, zero dependencies.

A powerful MCP server that lets you have natural language conversations with your database. Ask it to do complex analysis, generate beautiful visualizations, or build custom interactive dashboards based your data. Works with any JDBC-compatible database with support for most SQL DBs like PostgreSQL, MySQL, Oracle, SQL Server, SQLite, MongoDB, etc

The MCP Server support your LLMs integrate with SQL Database (SQLite, SQL Server, Postgres SQL)

Community-driven directory of concise AI explanations + runnable examples (LLMs, RAG, Agents, MCP, Vector DBs, Prompt Engineering).

Python vulnerability scanner & MCP security toolkit. Real-time dependency checking via OSV/NVD/GitHub Advisory DBs, Docker analysis, secrets detection, MCP config validation, LLM-powered risk assessment & interactive security audits. Full CVE details, CVSS scores & remediation guidance.

CAP (Context-Aware Protocol) is a middleware layer that enriches AI queries with memory, RAG, caching, and policy controls. It works with or without MCP, fusing context from vector DBs, APIs, and sessions into structured packages, ensuring AI responses are accurate, relevant, and secure.

An Model Context Protocol (MCP) server for interacting with ORION-DBs using LLMs

An MCP server for maximilien/weave-cli which is a universal CLI for vector DBs to help view, list, create, delete, and query collections and documents in collections for development, test, and debugging purposes

End-to-end, hands-on practice repo for the GenAI Systems Bootcamp. This repository tracks your journey across the full GenAI application stack—prompting, embeddings/vector DBs, RAG, agents (LangChain, LangGraph, CrewAI, Autogen), MCP, automation (n8n), deployment, and MLOps.

This repository is a structured learning hub for agentic AI systems and GenAI engineering. It includes notes, playground experiments, end-to-end projects, and production patterns covering LangChain, LangGraph, RAG, MCP tools, observability, testing/evals, and cloud integrations (Azure AI, AWS Bedrock, Databricks, vector DBs).

DB Operations with MCP

MySQL Database MCP Server - AI-powered database introspection with 6 powerful tools

mcp per recuperare uno schema da microsoft sql server

An MCP server that tracks DB Schenker shipments.

Sendify Code Challenge: DB Schenker Shipment Tracker MCP Server

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Proyecto que conecta Postgres con los LLMs por medio de su MCP & sobre una aplicacion Backend en NestJS.

Spring service with MCP tools to interact with different type of DBs

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Experimenting with MCPs (Google GenAI Toolbox for DBs specifically)

Demo project for mcp server to manipulate SQL DBs

playground for using genai-toolbox (mcp servers for dbs)

Production-style AI Knowledge Agent using RAG, Vector DBs, and MCP

Learning setup for Chatbots using Langchain and Ollama with MCP, Vector DBs, Neo4j, etc.

MCP-Server to retrieve data from different NO-SQL and SQL DBs with simple Prompts

Hands-on implementations of Python, LLMs, Agentic AI, RAG pipelines, LangChain, LangGraph, and scalable AI systems. Built by practicing real-world AI engineering concepts with Docker, FastAPI, Vector DBs, and MCP.

Next-gen AI Food Assistant using MCP for intelligent function calling. It connects an LLM to custom backend tools to dynamically query restaurant DBs, manage carts, and execute checkout workflows via structured execution. Built with a high-performance FastAPI backend, SQLite3 for persistence, and a seamless Vanilla JS frontend.