#other #clients #mcp

The Model Context Protocol (MCP) is an open standard that lets AI models easily and securely connect to different data sources and tools, making it much simpler for developers to build smart apps that can access files, databases, and APIs without custom code for each one[2][3][4]. There are many free and easy-to-use MCP clients—like desktop apps, web apps, and command-line tools—that let you quickly add new AI features and automate tasks, so you can get more done with less effort and technical hassle. This means you can use AI to help with coding, data analysis, and daily work, all while keeping your data safe and your setup flexible[2][3][4].

https://github.com/punkpeye/awesome-mcp-clients

#rust #agent #ai #amazon_q #cli #linux #llm #macos #mcp #open_source #productivity #rust #shell #terminal #typescript

Amazon Q CLI is a powerful tool that lets you interact with AWS and your development environment using natural language right from your terminal. It helps you write code, run commands, and manage AWS resources faster by understanding your context and providing smart suggestions, autocompletion, and even translating plain English into shell commands. It supports multi-turn conversations, so you can ask follow-up questions and get real-time help without leaving the command line. This boosts your productivity by simplifying complex tasks, reducing errors, and speeding up development workflows, making it easier to manage projects and infrastructure efficiently[1][2][3].

https://github.com/aws/amazon-q-developer-cli

#go #github #mcp #mcp_server

The GitHub MCP Server helps developers by connecting AI tools directly to GitHub. This allows AI assistants to manage issues, pull requests, analyze code, and automate workflows using natural language commands. It simplifies tasks like creating pull requests, reviewing code changes, and monitoring CI/CD pipelines. By automating these tasks, developers can focus more on coding and problem-solving, making their work more efficient and productive.

https://github.com/github/github-mcp-server

#python #csharp #java #javascript #javascript_applications #mcp #mcp_client #mcp_security #mcp_server #model #model_context_protocol #modelcontextprotocol #python #typescript

You can learn the Model Context Protocol (MCP), a new standard for connecting AI models with applications, through a free, open-source curriculum that includes hands-on coding examples in C#, Java, JavaScript, Python, and TypeScript. The curriculum covers basics, security, building servers and clients, advanced topics, and best practices, with multi-language support and community help via Discord. You can also join MCP Dev Days, a free online event for deep technical learning and networking. This resource helps you quickly gain practical skills to build and integrate AI tools effectively, boosting your development capabilities in AI workflows.

https://github.com/microsoft/mcp-for-beginners

#typescript #agentic_ai #agents #ai #claude #copilot #cursor #git #llm #mcp

GitMCP is a free, open-source service that connects AI assistants to any GitHub project’s latest documentation and code using the Model Context Protocol (MCP). This means your AI can access up-to-date, accurate information directly from the source, reducing mistakes and hallucinations when coding or asking questions about libraries, even new or niche ones. You just add a GitMCP URL for your chosen GitHub repo to your AI tool, and it fetches relevant docs and code smartly without setup hassle. This helps you get reliable code examples and API usage instantly, improving your coding efficiency and accuracy. It’s private, easy to use, and works with many AI assistants.

https://github.com/idosal/git-mcp

#python #agents #ai #api_gateway #asyncio #authentication_middleware #devops #docker #fastapi #federation #gateway #generative_ai #jwt #kubernetes #llm_agents #mcp #model_context_protocol #observability #prompt_engineering #python #tools

The MCP Gateway is a powerful tool that unifies different AI service protocols like REST and MCP into one easy-to-use endpoint. It helps you manage multiple AI tools and services securely with features like authentication, retries, rate-limiting, and real-time monitoring through an admin UI. You can run it locally or in scalable cloud environments using Docker or Kubernetes. It supports various communication methods (HTTP, WebSocket, SSE, stdio) and offers observability with OpenTelemetry for tracking AI tool usage and performance. This gateway simplifies connecting AI clients to diverse services, making development and management more efficient and secure.

https://github.com/IBM/mcp-context-forge

#php #agent #agi #ai #gpt #llm #low_code #mcp #no_code #sandbox #workflow

Magic is an open-source AI platform that helps businesses quickly build and use AI tools to boost productivity by up to 100 times. It offers a complete set of AI products, including a smart AI agent for complex tasks, an AI-powered chat system for team communication, and a visual tool to create AI workflows without coding. These tools work together to improve decision-making, automate tasks, and enhance collaboration securely within organizations. You can try Magic via cloud services or self-host it, making it flexible and powerful for different business needs. This platform saves time, improves efficiency, and supports smarter teamwork.

https://github.com/dtyq/magic

#typescript #agent #agentic_ai #agents #ai #ai_agents #ai_tools #anthropic #automation #bytebot #computer_use #computer_use_agent #cua #desktop #desktop_automation #docker #gemini #llm #mcp #openai

Bytebot is an open-source AI desktop agent that acts like a virtual employee with its own computer, able to use real applications, browse websites, handle passwords, and process documents automatically. You just describe tasks in plain English, and Bytebot completes them by clicking, typing, downloading files, organizing data, and running complex workflows across multiple programs. It runs locally on your own infrastructure, ensuring privacy and full control, and supports many AI models. This helps you save time by automating repetitive or complex tasks without scripting, improving efficiency and accuracy in business, research, or development work.

https://github.com/bytebot-ai/bytebot

#python #aws #mcp #mcp_client #mcp_clients #mcp_host #mcp_server #mcp_servers #mcp_tools #modelcontextprotocol

AWS MCP Servers use the Model Context Protocol (MCP), an open standard that connects AI tools with AWS data and services in a simple, secure way. These servers improve AI responses by providing up-to-date AWS documentation, best practices, and workflow automation for cloud development, infrastructure, and operations. You can run MCP servers locally for development or use AWS-managed remote servers for easy access and scalability. MCP servers support many AWS services like Lambda, DynamoDB, EKS, and more, helping you build, manage, and optimize AWS resources efficiently with AI assistance. Installation is easy with one-click options for popular tools like VS Code and Cursor. This makes cloud development faster, more accurate, and cost-effective.

https://github.com/awslabs/mcp

#kotlin #agentframework #agentic_ai #agents #ai #aiagentframework #android_ai #anthropic #generative_ai #java #jvm #kotlin #ktor #llm #mcp #ollama #openai #spring

Koog is a Kotlin-based open-source framework that helps you build AI agents fully in Kotlin, making it easy to create smart assistants that can use tools, manage complex tasks, and remember past interactions. It supports multiple AI models like OpenAI and Google, runs on many platforms (JVM, JavaScript, iOS), and offers features like real-time streaming, custom tools, and efficient memory use. Koog also provides debugging tools, flexible workflows, and scales from simple chatbots to enterprise systems. Using Koog lets you develop powerful, maintainable AI agents quickly and naturally within the Kotlin ecosystem, benefiting your projects with speed, flexibility, and strong integration options.

https://github.com/JetBrains/koog

#other #ai #anthropic_claude #awesome #context #mcp #model_context_protocol #servers #tool_use #tools

Model Context Protocol (MCP) is an open standard that lets AI models securely connect to various data sources and tools, like files, databases, APIs, and cloud services, to get real-time, relevant information. This helps AI give more accurate, up-to-date, and context-aware answers, reducing repeated data processing and improving efficiency. MCP also supports automation of complex workflows and integration with many platforms, making AI more powerful and flexible. However, running MCP servers requires careful security measures to avoid risks like unauthorized code execution. Using MCP can save time, reduce costs, and enhance AI capabilities for tasks like chatbots, data analysis, and system control.

https://github.com/appcypher/awesome-mcp-servers

#python #agent #ai #ai_coding #claude #claude_code #language_server #llms #mcp_server #programming #vibe_coding

Serena is a free, open-source toolkit that turns large language models (LLMs) into powerful coding agents able to work directly on your codebase with IDE-like precision. It uses semantic code analysis to understand code structure and symbols, enabling efficient code search and editing without reading entire files. Serena supports many programming languages and integrates flexibly with various LLMs and development environments via the Model Context Protocol (MCP). This means you can automate complex coding tasks, improve productivity, and reduce costs without subscriptions, making your coding workflow faster and smarter.

https://github.com/oraios/serena

#python #agents #ai #llm #mcp

You can access a large collection of ready-to-use AI agent projects and tutorials that help you build smart applications like chatbots, research assistants, and automation tools using popular AI frameworks such as LangChain, OpenAI Agents SDK, and Agno. This collection includes simple starter agents, advanced multi-agent workflows, and tools with memory and document understanding. It also offers step-by-step setup instructions and video tutorials to help you learn quickly. Using these resources saves you time and effort in creating powerful AI apps, making it easier to develop, test, and deploy AI solutions even if you are new to AI programming.

https://github.com/Arindam200/awesome-ai-apps

#python #agents #ai #ai_agents #api #developer_tools #discord #function_calling #integration #llm #mcp #mcp_client #mcp_server #oauth2 #open_source

Klavis AI helps developers connect AI tools to other services like GitHub, Gmail, and Slack easily. It offers hosted servers that handle authentication and client code automatically, making it simpler to integrate AI with various platforms. This saves time and effort by eliminating the need for custom authentication management and client library maintenance. Users can quickly set up and scale their AI applications without worrying about complex integrations, making it easier to deploy AI-powered workflows securely and efficiently.

https://github.com/Klavis-AI/klavis

#csharp #agent #ai #avalonia #chat #claude #deepseek #gpt_oss #grok #llm #mcp #ollama #openai #rag #ui_automation

Everywhere is an AI assistant that works directly on your screen without needing screenshots or app switching. You just press a shortcut and it understands the context instantly to help you with tasks like fixing errors, summarizing articles, translating text, or improving your writing tone. It supports many AI models and runs on Windows, with macOS and Linux versions coming soon. This tool saves you time and effort by giving quick, relevant help exactly where you need it, making your work and browsing smoother and more efficient. It also supports multiple languages and has a modern, easy-to-use interface.

https://github.com/DearVa/Everywhere

#typescript #mcp #mcp_server #n8n #workflows

n8n-MCP is a tool that connects AI assistants like Claude to the n8n workflow automation platform, giving AI deep knowledge of over 500 n8n nodes, their properties, operations, and documentation. It helps you quickly find, configure, and validate workflow templates or build workflows from scratch with real-world examples and smart filtering. You can deploy it easily via npx, Docker, or cloud services. This saves you time, reduces errors, and boosts confidence by letting AI assist in designing and managing complex automations safely, as you test changes before applying them to production. It makes building and maintaining workflows faster and more reliable.

https://github.com/czlonkowski/n8n-mcp

#typescript #agent #ai #ai_assistant #ai_chat #chat #chatbot #chatgpt #claude #cross_platform #deepseek #gemini #llm_client #mcp #mcp_client #openai_client #tool_calling

DeepChat is a powerful open-source AI chat platform that supports many large language models like OpenAI and Ollama. It offers features such as unified model management, local model integration, advanced tool calling, and enhanced search capabilities. DeepChat is privacy-focused, allowing local data storage and network proxy support. It's suitable for both personal and business use, supporting multiple platforms like Windows, macOS, and Linux. Users benefit from its flexibility, customization options, and privacy protection, making it a versatile tool for various AI applications.

https://github.com/ThinkInAIXYZ/deepchat

#go #a2a #agents #agents_sdk #ai #aiagentframework #gemini #genai #go #llm #mcp #multi_agent_collaboration #multi_agent_systems #sdk #vertex_ai

The Agent Development Kit (ADK) for Go is an open-source toolkit that makes it easy to build, test, and deploy smart AI agents using the Go programming language. It lets you create simple or complex agent workflows, use ready-made or custom tools, and run your agents anywhere, especially in cloud environments. With ADK, you get full control, flexibility, and the ability to scale your applications, making it faster and simpler to develop powerful AI solutions for real-world tasks.

https://github.com/google/adk-go

#python #data_analysis #dingtalk_robot #docker #feishu_robot #hot_news #mail #mcp #mcp_server #news #ntfy #python #telegram_bot #trending_topics #wechat_robot

TrendRadar is a lightweight, easy-to-deploy tool that gathers trending topics from 11+ major platforms like Zhihu, Douyin, and Baidu in just 30 seconds. It lets you set custom keywords to filter only news you care about, eliminating information overload. The tool offers three smart notification modes—daily summaries, current rankings, or incremental alerts—and supports multiple channels including WeChat Work, Feishu, DingTalk, Telegram, and email. You can customize how trends are ranked using a personalized algorithm that weighs ranking position, frequency, and hotness. With GitHub Pages for web reports, Docker support, and AI-powered analysis through MCP protocol, TrendRadar transforms scattered platform algorithms into one unified, user-controlled news feed tailored to your interests.

https://github.com/sansan0/TrendRadar

#typescript #browser #chrome #chrome_devtools #debugging #devtools #mcp #mcp_server #puppeteer

Chrome DevTools MCP lets your AI coding tools like Gemini, Claude, or Cursor control a live Chrome browser for automation, debugging, and performance checks. Install it easily with npx chrome-devtools-mcp@latest in your MCP config, then prompt "Check performance of a site" to auto-record traces, take screenshots, analyze networks, and fix issues reliably. This benefits you by making AI smarter at web coding—verifying changes in real-time, spotting bugs fast, and boosting site speed without manual work.

https://github.com/ChromeDevTools/chrome-devtools-mcp