A Model Context Protocol (MCP) server that automates generating professional LinkedIn post drafts from YouTube videos. This tool streamlines content
LinkedIn Post Generator MCP server exists for a simple reason — assistants are far more useful when they can act on LinkedIn Post Generator directly instead of describing what you should do. A Model Context Protocol (MCP) server that automates generating professional LinkedIn post drafts from YouTube videos. This tool streamlines content repurposing by extracting transcripts from YouTube videos, summarizing the content, and.
Installation goes through your MCP client rather than a global install: point it at @smithery/cli on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Once LinkedIn Post Generator is connected, these are the calls the assistant has available:
Tool — set_api_keysPurpose — Configure your API keysParameters — - openaiApiKey: Your OpenAI API key (required)youtubeApiKey — Your YouTube API key (optional)Documentation — By contributing to this project, you agree that your contributions will be licensed under the project's MIT LicenseYou will need 5 environment variables: OPENAI_API_KEY, YOUTUBE_API_KEY, YOUR_SMITHERY_API_KEY, YOUR_OPENAI_API_KEY, YOUR_YOUTUBE_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. LinkedIn Post Generator's toolset — Tool, Purpose, Parameters and 2 more — is a fair guide to whether it matches your workflow. It is maintained by NvkAnirudh; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Tool | set_api_keys |
| Purpose | Configure your API keys |
| Parameters | - openaiApiKey: Your OpenAI API key (required) |
| youtubeApiKey | Your YouTube API key (optional) |
| Documentation | By contributing to this project, you agree that your contributions will be licensed under the project's MIT License. |
{
"mcpServers": {
"linkedin-post-generator": {
"command": "npx",
"args": ["-y", "@smithery/cli"],
"env": {
"OPENAI_API_KEY": "your-value",
"YOUTUBE_API_KEY": "your-value",
"YOUR_SMITHERY_API_KEY": "your-value",
"YOUR_OPENAI_API_KEY": "your-value",
"YOUR_YOUTUBE_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| YOUTUBE_API_KEY | Credential the server authenticates with. | Yes |
| YOUR_SMITHERY_API_KEY | Credential the server authenticates with. | Yes |
| YOUR_OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| YOUR_YOUTUBE_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.