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Linkedin MCP
Managed LinkedIn MCP server for AI agents: search, connect, message and enrich on LinkedIn from Claude, ChatGPT or Cursor. 20,000+ accounts on this stack, under 1% ever restricted.
Install
npx -y @gtm-api/linkedin-mcpclaude mcp add linkedin-mcp -- npx -y @gtm-api/linkedin-mcp{
"mcpServers": {
"linkedin-mcp": {
"command": "npx",
"args": [
"-y",
"@gtm-api/linkedin-mcp"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →gtm-api: LinkedIn MCP Server
gtm-api is a managed LinkedIn MCP server. It gives an AI agent one endpoint, three MCP tools and 160+ typed LinkedIn actions over the Model Context Protocol, so Claude, ChatGPT or Cursor can search, connect, message and enrich on a LinkedIn account you own, with account safety enforced server side.
This repository holds the public interface: what the server exposes, how to connect a client, and how the safety layer works.
What is a LinkedIn MCP server?
A LinkedIn MCP server exposes LinkedIn actions as Model Context Protocol tools, so an AI agent calls them directly, the way a person would click through the UI. LinkedIn publishes no official API for invitations, member messages or open profile search, so a LinkedIn MCP server works through an account you own rather than through LinkedIn's developer platform.
Two shapes exist today. Cookie-driven open-source servers run a browser session with your own cookie and no pacing. Managed servers run each account in isolated infrastructure with limits enforced before every action. gtm-api is the second kind.
Which clients it works with
Any MCP-compatible client:
- Claude (Desktop, Code, and the API)
- Cursor
- ChatGPT (via connectors)
- LangChain, n8n, and custom agent runtimes
Not using MCP? Every tool is also a typed REST endpoint with webhooks. One schema generates all three surfaces.
Quickstart
1. Sign up. Create an account at app.gtm-api.com (forever free plan, no card) and connect a LinkedIn account. It opens in a dedicated anti-detect cloud browser with its own proxy.
2. Add the server to your MCP client. For Claude Desktop, edit claude_desktop_config.json (example in examples/):
{
"mcpServers": {
"gtm-api": {
"url": "https://mcp.gtm-api.com/mcp"
}
}
}
There is no key to paste. The endpoint authenticates with OAuth: your client gets a WWW-Authenticate challenge on the first call, registers itself with the authorization server (dynamic client registration), and opens a consent page in your browser.
Headless clients can skip the browser. The same API key (gtm_live_...) that authenticates the REST API also works on this endpoint as a plain bearer token, which is what makes it usable from CI and servers:
claude mcp add --transport http gtm-api https://mcp.gtm-api.com/mcp --header "Authorization: Bearer gtm_live_..."
For command-spawning clients, the launcher below reads the key from GTM_API_KEY instead (1.3.0 or newer; older pins ignore it and do OAuth only). Pick one mode per connection: with a key attached, the OAuth flow never starts. Interactive clients such as claude.ai connectors have no header field and always use OAuth. Full matrix: docs.gtm-api.com/mcp/connect.
Client can only spawn a command, not open a URL? The @gtm-api/linkedin-mcp launcher bridges stdio clients to the same endpoint and runs the same OAuth flow (config in examples/):
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