# AI agent

Demonstrate connecting the Keboola Registry API to the MCP server through the CrewAI wrapper, enabling AI agents to call registered skills.

An MCP server that connects to the Obsidian knowledge base. Through the REST API plugin, AI agents can perform complex knowledge discovery and analysis tasks, supporting functions such as advanced search, regular expression matching, and full - text retrieval.

An OpenAI agent server based on the MCP protocol, providing various professional agents (such as web search, file search, and computer operation) and a multi-agent coordinator, which can interact with clients (such as the Claude desktop application) through the MCP protocol.

This project showcases multiple Model Context Protocol (MCP) servers integrated with Microsoft Azure, which enable large language models (LLMs) to securely access Azure tools and data sources, including AI agent services and various database services.

A high-performance PostgreSQL MCP server based on Bun and TypeScript, providing multi-database interaction capabilities for AI agents.

Koppla is a natural language-based Active Directory management tool that enables querying and operating on user, group, and computer objects through an AI agent.

MCP BatchIt is a batching server for MCP tool calls. By aggregating multiple MCP tool calls into one request, it significantly reduces the communication overhead and token consumption of AI agents.

AI agents interact intelligently through the MCP server and use the model context protocol tool to execute operations input by the user.

Cyclops MCP is a tool that allows AI agents to securely manage and deploy Kubernetes applications through the Model Context Protocol, providing template validation and application creation/update functions

A Django application that implements a Firebase Model Context Protocol (MCP) server, enabling AI agents to interact with Firebase services through a standardized protocol. It provides 14 Firebase tools covering authentication, database, and storage functions, supporting quick testing and full integration.

PraisonAI is a production-ready multi-AI agent framework with self-reflection capabilities, designed to create AI agents to automate the solution of various problems from simple tasks to complex challenges. It simplifies the construction and management of multi-agent LLM systems by integrating PraisonAI agents, AG2, and CrewAI into a low-code solution, emphasizing simplicity, customization, and effective human-machine collaboration.

An MCP server based on pykrx, providing Korean stock market data query functions for AI agents, supporting multi - dimensional analysis of stock prices, financial indicators, investor transactions, short - selling data, etc.

TEN Agent is a multi-functional AI agent framework that integrates real-time vision, voice recognition, and screen sharing detection capabilities, and supports rapid expansion and development.

The Graphiti MCP Server is a server based on multi - project knowledge graphs, supporting the rapid construction and management of temporary knowledge graphs for AI agents to query through the model context protocol. This project extends the official getzep/graphiti MCP Server, adds support for multiple servers and a single database, and provides a CLI tool with an optimized developer experience.

AI agent robot project based on multi-MCP servers

MCP speech synthesis server based on Edge-TTS

An MCP server based on The Graph that provides indexed blockchain data query services for AI agents, supporting subgraph structure retrieval and GraphQL query execution.

The Azure MCP Server is a server that implements the MCP protocol, providing seamless connection between AI agents and Azure services (such as Storage and Cosmos DB), and supporting the conversion of natural language commands into Azure operations.

MCP Server is an AI agent tool designed specifically for macOS that improves work efficiency through system-level control, is compatible with MCP models, and integrates AI functions.

This project demonstrates how to integrate OpenAI, Azure OpenAI, and GitHub models through the MCP protocol to build a terminal application for a TODO list agent. The project includes three major components: MCP host, client, and server, supports HTTP and SSE protocols, and provides tool invocation and database storage functions.

The TMF620 MCP Server is a middleware that connects AI agents with the remote TMF620 product catalog management API, providing functions for querying and creating catalogs, product offerings, and product specifications.

An MCP server for GitLab and Jira integration, providing functions for AI agents to interact with GitLab and Jira instances, including project management, merge request processing, file viewing, user activity tracking, and Jira ticket management.

Omni-LPR is a self-hosted server with multiple interfaces (REST and MCP) that provides automatic license plate recognition (ALPR) functions and can be used as an independent microservice or a toolbox for AI agents.

Xcatcher is a high - performance X (Twitter) data scraping service based on the remote MCP protocol. It supports pay - as - you - go with USDC on the Base and Solana chains via the x402 protocol, provides OpenAPI specifications for easy integration of AI agents, and focuses on the rapid collection of the latest posts of a large number of users.

DomainKits MCP is a domain name intelligent agent framework designed for AI assistants. It integrates tools such as domain name search, analysis, monitoring, trend discovery, and batch processing, and can transform general - purpose AI assistants into professional domain name agents with industry - specific knowledge.

The Florentine.ai MCP Server allows AI agents to query MongoDB and MySQL databases through natural language, supporting functions such as multi - tenant data isolation, automatic schema exploration, and semantic vector search.

An AI - driven architecture analysis and intelligent coding agent system for GitHub repositories. By deeply analyzing the codebase structure, generating architecture blueprints, and using real - time MCP tools, it ensures that AI coding agents follow unified architecture specifications, improving code quality and consistency.

A stateful Bluetooth Low Energy (BLE) Model Context Protocol (MCP) server that provides development tools and AI agents with the ability to interact with real BLE hardware. It supports operations such as scanning, connecting, reading and writing, and subscribing to notifications, and can extend device protocol specifications and plugins.

CamoFox MCP is an AI-driven anti-detection browser automation tool that provides browser control functions for AI agents through the MCP protocol, supporting navigation, interaction, data extraction, and multi-tab workflows, and built-in anti-detection mechanisms.

Airweave is an open - source context retrieval layer for AI agents and RAG systems. It connects and synchronizes data from various applications, tools, and databases, and provides relevant, real - time, multi - source contextual information to AI agents through a unified search interface.