AI 機器學習 資源

Agente de gestin de clientes compatible con el protocolo MCP (Model Context Protocol).

A Python package for interacting with LLMs via the MCP protocol.

This project is a fully autonomous conversational AI agent that interacts via WhatsApp, interprets user requests using Google Gemini, and delegates actions to two dedicated MCP Servers for Gmail and Google Calendar.

Unlock the power of AI-driven infrastructure analysis with Steampipe! This Model Context Protocol server seamlessly connects AI assistants like Claude to your cloud infrastructure data, enabling natural language exploration and analysis of your entire cloud estate.

A Multi-Context Protocol (MCP) server that provides Claude.ai with specialized tools for code editing, formatting, reviewing, and context analysis through SSE (Server-Sent Events).

F4rmhouse is an all-in-one, open source platform for building useful agents. It provides every tool you need to create a modern agent with all its bells and MCP whistles.

Moonshot Project is an innovative intelligent task processing platform currently under active development. We are dedicated to creating a powerful, flexible, and user-friendly AI assistant system. Stay tuned!

WigAI is a Model Context Protocol (MCP) server implemented as a Bitwig Studio extension. It provides an interface for external AI assistants to control Bitwig Studio through simple text-based commands.

An AI-powered chatbot for Slack powered by the AI SDK by Vercel.

A powerful Model Context Protocol (MCP) server to supercharge developer productivity through AI-assisted automation of common developer workflows.

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This repo has the python streamlit UI code for the Pca web interface. The web interface uses OpenAI and the Pca MCP server to provide tools related to OpenStreetMap.

A Streamlit-based application that uses Gemini API to process natural language commands for managing your file system.

A Pyodide server implementation for the Model Context Protocol (MCP). This server enables Large Language Models (LLMs) to execute Python code through the MCP interface.

The goal of this project is to:

ToolBox is a collection of tools and functions designed to be used in conjunction with Language Model (LLM) agents. This repository provides utilities for interacting with APIs, handling geolocation, weather data, and more. The tools are modular and can be easily integrated into larger projects.

A Python-based agent that integrates with the Model Context Protocol (MCP) using the pydantic-ai library. This tool allows you to fetch and summarize content from URLs and perform web searches using various AI models.

A demo for a Spotify AI Assistant, using GPT4o and MCP

AI Image Generation Platform

OpenAPI2MCP is a Python tool that serves as a Model Context Protocol (MCP) server with tools generated from OpenAPI specification files (JSON or YAML). It allows LLMs and AI applications that support MCP to interact with any API that has an OpenAPI specification.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Money Split Application leveraging MCP Application with Paypal AI tools

repo for learning AI agents and MCP

MCP Notarium is a server application that acts as a bridge between Large Language Models (LLMs) and the Simplenote note-taking service. It exposes a set of tools compliant with the Model Context Protocol (MCP), enabling LLMs to read, write, search, and manage a user's Simplenote notes.

AI based AO tool calling on steroids that integrates with your existing tools and editor using a MCP server.

GO for Large LanguagE Model (GOLLEM)

Hosted by Modl, any commits or changes made by the Modl team is to ensure compatibility

This project implements a multi-component system that allows users to interact with a database using natural language queries through a chat interface. It features an intelligent Host server that uses LLM-based tool calling to interact with a specialized MCP (Model Context Protocol) aligned data processing server.

Deep Research for Meeting Prep

This is the Model Context Protocol (MCP) server for Sentio, providing a standardized interface for interacting with Sentio's API services.

MCP for Vedic astrology API powered by Prokerala API, providing astrological services like horoscopes, panchang, and compatibility analysis through both command-line and web interfaces.

A powerful AI-powered screenshot capture and image analysis tool that integrates with the Model Context Protocol (MCP) ecosystem. Designed with a flexible three-layer architecture for maximum extensibility and maintainability.

AutoClass is a modular, intelligent execution framework built using the Model Context Protocol (MCP). It enables any Python class to describe and operate on itself via structured docstrings allowing AutoClass to dynamically interpret, route, and run methods based on user queries.

Evaluation Framework for MCP's

Play Wordle with your LLM companion and see who is the best in the world.

An MCP Server that integrates the ADB command, providing device actions capabilities into LLM

A comprehensive collection of free resources to learn everything about AI/ML, LLMs and Agents.

Many code editors and other AI clients use a configuration file to manage MCP servers.

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A Market Control Platform (MCP) server that interacts with the TradingView platform to analyze stocks, execute mock trades, and perform market research. The server implements the Model Context Protocol to serve as a context provider for large language models (LLMs).

This project demonstrates the use of the Model Context Protocol (MCP) for building AI-powered applications. It consists of four modules that showcase different ways of implementing and interacting with MCP servers.

This project is based on the Build Rich Context AI Apps with Anthropic course from DeepLearning.AI. The course teaches how to build sophisticated AI applications using Anthropic's Claude model and the Multi-Context Processing (MCP) framework.

LLM AI . Tool .

Summon Claude Code, OpenAI Codex friendsone plug-in bridge to MCP.

LLM+ Notion API

This Model Context Protocol (MCP) server enables interaction with the Meraki Dashboard API for LLMs, allowing for seamless integration of Meraki network management capabilities within an AI assistant's conversational interface.

This FastAPI application serves as a ready-to-use backend template for interacting with multiple Model Context Protocol (MCP) agent services via Server-Sent Events (SSE). It offers endpoints to query agent responses and fetch active server tool details. Additionally, it includes memory checkpointing to maintain conversational context and enhance the understanding of user queries over time.

When terminal meets intelligence: AI at your fingertips.

BrandZero is a Model Context Protocol (MCP) server that analyzes brand presence and consumer search behavior using multiple LLM platforms. It helps marketers understand how their brands are discovered and perceived online.

Next-generation AI-powered shell using Google Gemini

This is a Node.js-based command-line application that lets you generate and post professional tweets using Together AI's LLM and publish them directly to your Twitter account using the official Twitter API v2.

An example of how to build an Agent that acts as an MCP Client, so that it can connect to external services via MCP.

MCPtastic provides Model Context Protocol (MCP) bindings for the Meshtastic CLI. This allows AI assistants like Claude to directly interact with your Meshtastic devices, enabling powerful voice and text control of your mesh network.

This is a Model Context Protocol (MCP) server for Adobe Express integration with LLMs like Claude. It provides tools to interact with Adobe Express features directly from LLM interfaces.

An advanced scholarly research MCP server that provides a uniform set of tools for discovering, fetching, processing, and managing scholarly content across multiple sources.

Workbench Development is a new paradigm, which puts the developer as a leader of a fleet of AI Agents to write code, but in a structured manner that gives them full control over the outcomes.