F
licenseNot graded
qualityD
maintenanceEnables AI applications to integrate with YouTube-Summarizer's APIs through the MCP protocol, offering local tool-based interaction for summarizing YouTube content.
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MCP Server
by kabir-ti
Provides APIs from the Youtube-Summarizer, allowing AI applications to access YouTube content summarization capabilities
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Serversummarize the latest video from my subscribed channels"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Server
MCP Server is created on top of all the APIs from the Youtube-Summarizer. All APIs are exposed as tools in the MCP protocol and available for any AI application to integrate with.
Note: Currently MCP only supports local connections, so it doesn't support remote use of these tools.
Setup
Docker Setup
Build the Docker image:
docker build -t youtube-summarizer-mcp .Run the MCP server using Docker:
docker run -i --rm youtube-summarizer-mcpUsing the Inspector
You can use the MCP Inspector to explore available tools and test them:
./inspector.shUsage with Claude Desktop
Add the following to your claude_desktop_config.json:
{
"mcpServers": {
"youtube-summarizer": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"youtube-summarizer-mcp"
]
}
}
}Now you can use the added mcp tools from server.py in claude desktop
MCP Client Sample (Without Claude Desktop)
Run the MCP client locally to try out the Social Toolkit using natural language:
Setup
./setup.shRun
./run.shIt will run both MCP server and client, connected to each other. The terminal will prompt for natural language queries from the user, which then will be translated into MCP tool calls to answer the user query.
This server cannot be deployed
Maintenance
ActivityInactive
ResponsivenessNo issues
Related MCP Connectors
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
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