Gtm api: LinkedIn MCP Server: MCP server, setup and security

What Gtm api: LinkedIn MCP Server does

According to its repository: “Managed LinkedIn MCP server for AI agents: search, connect, message and enrich on accounts you own.”

The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. Gtm api: LinkedIn MCP Server is listed under Developer tools, a category of 475 servers in the directory, where it ranks #186 by stars.

Topics declared by the repository: ai-agents, ai-sdr, chatgpt, claude, cursor, gtm.

Installing and connecting Gtm api: LinkedIn MCP Server

Distribution modes declared in the official MCP registry:

• npm package (Node.js, usually started with npx)
• remote server (streamable-http): you connect to it by URL, with nothing to install locally

It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: TypeScript.

Security: what to check

Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Gtm api: LinkedIn MCP Server. That does not replace reading the code.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on July 27, 2026, the gtm-api/linkedin-mcp repository has 90 stars and 0 forks, with 0 open issues. It has more stars than 65% of the servers in the directory. It received changes this month. Version published in the registry: 1.2.0 (September 28, 2026).

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.