University of Kwazulu-Natal

Youtube Gumcp Server Model Context Protocol Gumloop

Bonisiwe Shabane
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youtube gumcp server model context protocol gumloop

Interact with YouTube to search videos, get video details, channel information, playlists, and more using Apify's YouTube scraper Search YouTube videos by search terms with filtering options and comprehensive metadata Get detailed information about specific YouTube videos by URL or video ID with statistics and content details Get videos from a specific YouTube channel with full metadata and channel information Get videos from a YouTube playlist with metadata and playlist context First, you'll need to set up the GUM in general and have it build some sense of your context.

To do this, follow the instructions on the front page here. You'll also need a client that supports MCP. One example client is the MacOS Claude Desktop app, which you can download here. The Claude desktop app requires the uv package manager for MCP, so you'll need to follow the instructions on the uv website (or simply brew install uv). If you didn't use brew, make sure uv is installed globally Annoyingly, some apps (Claude) don't look at your local PATH.

So if you didn't use brew, your uv might be in your local bin ~/.local/bin/uv. You can test this by running which uv. Luckily, you can just set a symlink to fix this: DXTs are extension files that make the MCP setup really easy. First, make sure the Claude desktop app is updated! Download the .dxt file from the releases page here and just double-click (or drag it into the extensions page in the Claude Desktop app; Claude > Settings > Extensions).

You'll be asked to provide your full name so the GUM knows who you are. Don't forget to enable the extension, and you'll be good to go! Clone the MCP Repository and run the following: Unifies Model Context Protocol (MCP) servers with a consistent backend for local and remote AI integrations. GuMCP stands as an innovative, open-source initiative, presenting a robust collection of Model Context Protocol (MCP) servers designed to streamline AI integrations. This powerful platform enables users to establish a unified backend architecture, whether operating servers locally or remotely.

The core mission of GuMCP is to serve as a comprehensive, community-driven resource for developers and AI enthusiasts, fostering a consistent implementation across a variety of servers. By supporting both standard input/output (stdio) and Server-Sent Events (SSE) transports, GuMCP ensures flexibility and broad compatibility. As an open-source project, it actively encourages community contributions, driving the continuous development and enhancement of AI tools and sophisticated workflows, making it a pivotal resource for anyone looking to build with advanced AI... GuMCP excels in providing a unified backend architecture, ensuring consistent server implementation across diverse applications. It boasts an extensive and growing collection of MCP servers, catering to a wide array of AI integration needs. The platform offers the flexibility of supporting both local (stdio) and remote (SSE) hosting, allowing users to adapt to their specific operational environments.

Furthermore, GuMCP champions community-driven development, actively facilitating contributions and fostering a collaborative ecosystem for the advancement of AI technologies. This commitment to open access and shared development makes building and managing AI solutions with an mcp server more accessible than ever. guMCP, or Gumloop Unified Model Context Protocol, is an open-source collection of Model Context Protocol (MCP) servers. It's designed to offer a unified backend for both local and remote AI integrations, simplifying the process of managing and connecting to various AI models and tools, especially when utilizing an mcp server. To begin with guMCP, you'll need to clone the repository from GitHub. Following this, set up a virtual environment, install the necessary dependencies, and configure the required environment variables.

Comprehensive instructions and practical usage examples are readily available within the project's README file, guiding you through the setup of your mcp server. Interact with Gumloop to manage automated workflows, saved flows, run executions, and organization audit logs List saved flows/items in your Gumloop account for a specific user or project List workbooks and their associated saved flows with nested flow information Retrieve automation run history for workbooks or saved items with execution details Start/trigger flow execution via API with optional input parameters for automation

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To Do This, Follow The Instructions On The Front Page

To do this, follow the instructions on the front page here. You'll also need a client that supports MCP. One example client is the MacOS Claude Desktop app, which you can download here. The Claude desktop app requires the uv package manager for MCP, so you'll need to follow the instructions on the uv website (or simply brew install uv). If you didn't use brew, make sure uv is installed globally An...

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You'll Be Asked To Provide Your Full Name So The

You'll be asked to provide your full name so the GUM knows who you are. Don't forget to enable the extension, and you'll be good to go! Clone the MCP Repository and run the following: Unifies Model Context Protocol (MCP) servers with a consistent backend for local and remote AI integrations. GuMCP stands as an innovative, open-source initiative, presenting a robust collection of Model Context Prot...

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