How do I install Gtm Api: LinkedIn MCP Server?

Copy the http config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Gtm Api: LinkedIn MCP Server need an API key?

The publisher lists these environment variables: GTM_MCP_URL.

What transport does Gtm Api: LinkedIn MCP Server use?

Gtm Api: LinkedIn MCP Server is reached over HTTP.

When was Gtm Api: LinkedIn MCP Server last checked?

Last checked 2026-11-08. Managed LinkedIn MCP server for AI agents: search, connect, message and enrich on accounts you own.

Gtm Api: LinkedIn MCP server

Power your agent

Add this server

The publisher's own URL. Not a PlugMCP endpoint.

Add to Cursor Add in Claude Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Server URL

https://mcp.gtm-api.com/mcp

Credentials from the publisher

GTM_MCP_URL — Endpoint override. Defaults to https://mcp.gtm-api.com/mcp. Authentication is OAuth, run by the launcher on first use, so no credential goes in the client config.

The Claude button above opens Claude with the name and URL filled in. You still confirm it there.

shClaude Code

1claude mcp add --transport http com.gtm-api-linkedin-mcp https://mcp.gtm-api.com/mcp

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "com.gtm-api-linkedin-mcp": {4      "command": "npx",5      "args": [6        "-y",7        "mcp-remote",8        "https://mcp.gtm-api.com/mcp"9      ]10    }11  }12}

claude_desktop_config.json

Remote · HTTPPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Remote

Transport: http
Version: 1.3.2

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

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/):

{
  "mcpServers": {
    "gtm-api": {
      "command": "npx",
      "args": ["-y", "@gtm-api/linkedin-mcp"]
    }
  }
}

For Claude Code it is one line: claude mcp add --transport http gtm-api https://mcp.gtm-api.com/mcp.

The same launcher also ships as a Docker image, gtmapi/linkedin-mcp (config in examples/). Mount a volume on the token cache so the consent step runs once rather than on every container:

{
  "mcpServers": {
    "gtm-api": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-v", "gtm-mcp-auth:/home/node/.mcp-auth", "gtmapi/linkedin-mcp"]
    }
  }
}

3. Restart the client and prompt in plain English:

"Every morning, accept new connection invitations from founders, reply with a short welcome, and add anyone hiring SDRs to a warm list."

The agent discovers the actions and chains them: fetch the latest connection invitations, accept the ones that match, send each new contact a short message. Every outward action runs a preview-then-confirm step and a server-side daily-limit check before it reaches LinkedIn.

MCP tools

The server exposes exactly three MCP tools. Discovery is progressive: an agent lists the toolsets, inspects one, then invokes an action. The 160+ LinkedIn actions never load into the model's context at once, so the schema footprint stays around 400 tokens.

[
  {
    "name": "list_toolsets",
    "description": "List the available toolsets (domains) on this server. Each toolset groups related tools (e.g. linkedin.messaging, id.billing). Start here, then get_toolset_tools to inspect one, then call_tool to run a tool. Requires OAuth authorization (Bearer token).",
    "input_schema": { "type": "object", "properties": {} }
  },
  {
    "name": "get_toolset_tools",
    "description": "List the tools in a toolset. Default (lite) returns name + title + one-line summary; pass verbose:true for full descriptions, safety flags, and parameter names. Run one via call_tool.",
    "input_schema": {
      "type": "object",
      "properties": {
        "toolset": { "type": "string", "description": "Toolset id from list_toolsets, e.g. \"linkedin.messaging\"." },
        "verbose": { "type": "boolean", "description": "Include full descriptions + parameter names." }
      },
      "required": ["toolset"]
    }
  },
  {
    "name": "call_tool",
    "description": "Invoke a tool by name (discovered via get_toolset_tools) with its arguments object. Behaves exactly like calling the tool on its domain mount. Dangerous tools still require the two-step preview→confirm (pass commit_token inside arguments on the confirm call).",
    "input_schema": {
      "type": "object",
      "properties": {
        "name": { "type": "string", "description": "Exact tool name." },
        "arguments": { "type": "object", "description": "The tool's arguments object." }
      },
      "required": ["name"]
    }
  }
]

call_tool validates arguments against the target action's own input schema, so preview-then-confirm, rate limits and typed errors apply exactly as if the action were mounted directly.

What the agent can do

160+ typed actions across 11 LinkedIn toolsets, grouped here into seven areas:

Toolset	What it covers
Messaging	member messages, voice notes, InMail, Sales Navigator chats, inbox search and sync
Network	connection requests, accept or ignore invitations, withdraw, connections and followers
Content	track posts and metrics, comment, react, get engagers and commenters
Enrichment	lite and full profile, experience, skills, education, company data
Search	people, company and post search, similar profiles, employees, decision-makers, saved searches
Account health	smart limits, health snapshots, quota-hit, block and activity logs
Infrastructure	anti-detect cloud browsers, dedicated proxies, webhooks

The same endpoint also serves the account, billing and orchestration toolsets, 250+ typed actions in total. This repo documents the LinkedIn half.

LinkedIn is the live channel today. Email (Gmail, Outlook, IMAP), messengers (WhatsApp, Telegram, Instagram DMs) and calendars (Google, Microsoft) are on the roadmap on the same typed contract.

Is it safe for my LinkedIn account?

Safety is enforced by the server itself, under every tool call. Six mechanisms:

Owned accounts. The agent acts through an account you connected and control. There is no shared account pool.
Session isolation. Each account runs in its own anti-detect cloud browser with a dedicated proxy. One account, one session, one device signature.
Warm-up. A new account starts at a fraction of platform maximum, and its allowance is raised programmatically as the account ages and builds history.
Server-side limits. Per-action daily budgets are checked before dispatch, across 16 action buckets.
Randomized pacing. Bulk work is spread with per-gap randomized intervals, because a fixed cadence is itself a detectable pattern.
Preview then confirm. Outward actions return a preview and require confirmation, so an over-eager agent cannot burn an account.

On this setup, 20,000+ LinkedIn accounts run on gtm-api.com, and under 1% of them have ever been restricted. Full method: gtm-api.com/safe-linkedin-automation.

How it compares to open-source LinkedIn MCP servers

Cookie-driven servers such as stickerdaniel/linkedin-mcp-server run a browser session with your own LinkedIn cookie and leave pacing to you: the project's README documents the cookie login flow and lists no rate limiting. Apify's LinkedIn actors are a different shape, hosted scrapers priced per result and focused on pulling data out.

	gtm-api (managed)	Cookie-driven open source
Account model	Owned, warmed, isolated	Your live cookie session
Safety layer	Anti-detect browser, dedicated proxy, limits	Not built in
Limit enforcement	Server side, before every action	You build it
Sends (connect, message, InMail)	Yes, with preview then confirm	Partial or none
Published restriction figure	Under 1% of 20,000+ accounts ever restricted	Not published
Support	Managed	Community
Price	From $39 per connected account per month, $10 at volume	Free, run it yourself
Self-hosted, auditable code	No, managed service	Yes

Pricing

Volume pricing per connected account per month, from $10/account at scale, with unlimited API calls and no per-action fees. Forever free plan, no card: gtm-api.com/pricing.

A note on LinkedIn's terms

LinkedIn's User Agreement does not permit third-party automation, so every tool in this space carries risk and no vendor can honestly promise otherwise. gtm-api reduces that risk by acting only on accounts you own, with warm-up, human-like pacing and enforced limits, which is why under 1% of the accounts on it have ever been restricted. This is not legal advice.

Gtm Api: LinkedIn MCP Server

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Gtm Api: LinkedIn MCP Server?

Does Gtm Api: LinkedIn MCP Server need an API key?

What transport does Gtm Api: LinkedIn MCP Server use?

When was Gtm Api: LinkedIn MCP Server last checked?

Documentation

gtm-api: LinkedIn MCP Server

What is a LinkedIn MCP server?

Which clients it works with

Quickstart

MCP tools

What the agent can do

Is it safe for my LinkedIn account?

How it compares to open-source LinkedIn MCP servers

Pricing

A note on LinkedIn's terms

Links