# GTM API: LinkedIn MCP Server **Category:** 🌐 Social Media **Repository:** https://github.com/gtm-api/linkedin-mcp **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/gtm-api-linkedin-mcp-server ## Description Managed LinkedIn MCP server for AI agents: search, connect, message and enrich on accounts you own. ## Claude Desktop Quick Installation Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low): ```json "mcpServers": { "gtm-api-linkedin-mcp-server": { "command": "npx", "args": ["-y","gtm-api-linkedin-mcp-server"] } } ``` ## Documentation & README # gtm-api: LinkedIn MCP Server [![Site](https://img.shields.io/badge/site-gtm--api.com-bef264)](https://gtm-api.com/linkedin-mcp-server/) [![Protocol](https://img.shields.io/badge/protocol-MCP-blue)](https://modelcontextprotocol.io/) [![License](https://img.shields.io/badge/license-MIT-lightgrey)](./LICENSE) [![Smithery](https://smithery.ai/badge/gtm-api/linkedin-mcp)](https://smithery.ai/servers/gtm-api/linkedin-mcp) gtm-api is a managed LinkedIn MCP server. It gives an AI agent one key, three MCP tools and 160+ typed LinkedIn actions over the [Model Context Protocol](https://modelcontextprotocol.io/), 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](https://learn.microsoft.com/en-us/linkedin/shared/authentication/getting-access) 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](https://developer.linkedin.com/). 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. Get an API key.** Sign up at [app.gtm-api.com](https://app.gtm-api.com/login) (7-day trial, 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/`](./examples/claude_desktop_config.json)): ```json { "mcpServers": { "gtm-api": { "url": "https://mcp.gtm-api.com/mcp", "headers": { "Authorization": "Bearer YOUR_API_KEY" } } } } ``` Prefer a command instead of a URL? The [`@gtm-api/linkedin-mcp`](https://www.npmjs.com/package/@gtm-api/linkedin-mcp) launcher bridges stdio clients to the same endpoint (config in [`examples/`](./examples/claude_desktop_config.npx.json)): ```json { "mcpServers": { "gtm-api": { "command": "npx", "args": ["-y", "@gtm-api/linkedin-mcp"], "env": { "GTM_API_KEY": "YOUR_API_KEY" } } } } ``` For Claude Code it is one line: `claude mcp add gtm-api -e GTM_API_KEY=YOUR_API_KEY -- npx -y @gtm-api/linkedin-mcp`. The same launcher also ships as a Docker image, [`gtmapi/linkedin-mcp`](https://hub.docker.com/r/gtmapi/linkedin-mcp) (config in [`examples/`](./examples/claude_desktop_config.docker.json)): ```json { "mcpServers": { "gtm-api": { "command": "docker", "args": ["run", "-i", "--rm", "-e", "GTM_API_KEY", "gtmapi/linkedin-mcp"], "env": { "GTM_API_KEY": "YOUR_API_KEY" } } } } ``` **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. ```json [ { "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 a gtm-api key (Bearer).", "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 10 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 | 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 gtm-api reports 20,000+ LinkedIn accounts running at under 1% monthly ban. Full method: [gtm-api.com/safe-linkedin-automation](https://gtm-api.com/safe-linkedin-automation/). ## How it compares to open-source LinkedIn MCP servers Cookie-driven servers such as [`stickerdaniel/linkedin-mcp-server`](https://github.com/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](https://apify.com/)'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 ban rate | Self-reported: under 1% monthly across 20,000+ accounts | Not published | | Support | Managed | Community | | Price | From $19 per connected account per month | Free, run it yourself | | Self-hosted, auditable code | No, managed service | Yes | ## Pricing From $19 per connected account per month, scaling to $5 at volume, with unlimited API calls and no per-action fees. 7-day trial, no card. Enrichment and Signals are optional metered add-ons: [gtm-api.com/pricing](https://gtm-api.com/pricing/). ## A note on LinkedIn's terms [LinkedIn's User Agreement](https://www.linkedin.com/legal/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 the reported ban rate is under 1%. This is not legal advice. ## Links - Get an API key: [app.gtm-api.com](https://app.gtm-api.com/login) - How a LinkedIn MCP server works: [gtm-api.com/linkedin-mcp-server](https://gtm-api.com/linkedin-mcp-server/) - The safety method in detail: [gtm-api.com/safe-linkedin-automation](https://gtm-api.com/safe-linkedin-automation/) - npm launcher: [npmjs.com/package/@gtm-api/linkedin-mcp](https://www.npmjs.com/package/@gtm-api/linkedin-mcp) - Docker image: [hub.docker.com/r/gtmapi/linkedin-mcp](https://hub.docker.com/r/gtmapi/linkedin-mcp) - Model Context Protocol: [modelcontextprotocol.io](https://modelcontextprotocol.io/) --- [gtm-api.com](https://gtm-api.com/), the LinkedIn API and MCP server for AI agents.