LinkedIn MCP Server

A production-ready Model Context Protocol (MCP) server connecting AI agents (Google Gemini Spark, OpenAI Codex, Claude Desktop, Cursor, Windsurf) to the official LinkedIn REST API.

Enables AI agents to autonomously draft and publish text updates, share rich link previews, upload images, create interactive polls, manage existing posts, and engage with reactions and threaded comments.

https://github.com/user-attachments/assets/cda965be-d6a2-4a37-8d3c-29a68a9959e1

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Features

• Dual-Mode MCP Transport:
  • Server-Sent Events (SSE) via GET /mcp and POST /messages for clients like Google Gemini Spark and Claude.
  • Streamable HTTP via POST /mcp for modern command-line and agent runtimes like OpenAI Codex (rmcp).
• 12 Comprehensive Tools:
  • Content Creation: Text updates, article link preview cards, binary image uploads (3-step pipeline), and multi-option polls.
  • Content Management: Delete posts by URN, fetch engagement statistics (likes, comments, shares), and verify account profile connections.
  • Social Engagement: Add reactions (LIKE, CELEBRATE, LOVE, INSIGHTFUL, FUNNY, EMPATHY), delete reactions, add comments, reply in threads, and remove comments.
• Robust Validation: Powered by Zod schemas for all tool input parameters.
• Enterprise-Ready: Automated unit and integration test suite with 100% endpoint coverage.

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Tool Reference

Tool	Category	Description	Parameters
create_text_post	Content	Publish a standard text post to your LinkedIn feed	content (string, required)
create_link_post	Content	Publish a post with a clickable preview card	content (string), url (string, URL), title (string)
create_image_post	Content	Download an image from a URL, upload it to LinkedIn, and publish	content (string), image_url (string, URL)
create_poll_post	Content	Create a poll with customizable duration	question (string), options (string array, 2-4 items), duration (ONE_DAY, THREE_DAYS, ONE_WEEK, TWO_WEEKS)
delete_post	Management	Delete a post using its Post URN	post_urn (string, e.g. urn:li:ugcPost:...)
get_user_profile	Management	Retrieve authenticated profile details (name, member ID)	None
get_post_stats	Management	Fetch engagement metrics (likes, comments, shares)	post_urn (string)
react_to_post	Engagement	Add a reaction to a post	post_urn (string), reaction_type (LIKE, CELEBRATE, LOVE, INSIGHTFUL, FUNNY, EMPATHY)
remove_reaction	Engagement	Remove your reaction from a post	post_urn (string)
comment_on_post	Engagement	Add a top-level comment on a post	post_urn (string), comment_text (string)
delete_comment	Engagement	Delete a comment you made	post_urn (string), comment_id (string)
reply_to_comment	Engagement	Post a threaded reply to an existing comment	post_urn (string), parent_comment_urn (string), reply_text (string)

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Setup & Authentication

1. LinkedIn Developer Portal Setup

1. Create a LinkedIn application in the LinkedIn Developer Portal.
2. Under the Products tab, request access to:
   • Share on LinkedIn (provides w_member_social permission)
   • Sign In with LinkedIn using OpenID Connect (provides openid and profile permissions)
3. In the Auth tab, add a redirect URL (e.g. https://httpbin.org/get or your domain).

2. Generate a User Access Token

1. Construct the authorization URL in your browser:

   https://www.linkedin.com/oauth/v2/authorization?response_type=code&client_id=YOUR_CLIENT_ID&redirect_uri=YOUR_REDIRECT_URI&scope=openid%20profile%20w_member_social
   

2. Authorize the application and copy the code query parameter from the redirection URL.
3. Exchange the code for a 60-day access token:

   curl -X POST https://www.linkedin.com/oauth/v2/accessToken \
     -d "grant_type=authorization_code" \
     -d "code=YOUR_AUTHORIZATION_CODE" \
     -d "client_id=YOUR_CLIENT_ID" \
     -d "client_secret=YOUR_CLIENT_SECRET" \
     -d "redirect_uri=YOUR_REDIRECT_URI"
   

4. Fetch your member ID:

   curl -H "Authorization: Bearer YOUR_ACCESS_TOKEN" https://api.linkedin.com/v2/userinfo
   

   The sub field is your member identifier (e.g., urn:li:person:<sub_id>).

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Configuration

Copy the sample environment file:

cp .env.example .env

Configure your .env variables:

LINKEDIN_ACCESS_TOKEN=your_oauth2_access_token
LINKEDIN_AUTHOR_URN=urn:li:person:your_member_sub_id
PORT=3000

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Running Locally

# Install dependencies
npm install

# Run unit and integration tests
npm test

# Start the server
npm start

# Start in development mode (with live reload)
npm run dev

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Client Integration

OpenAI Codex CLI

Add the server using the Codex CLI:

codex mcp add linkedin --url https://your-server-domain.com/mcp

Or add it directly to ~/.codex/config.toml:

[mcp_servers.linkedin]
url = "https://your-server-domain.com/mcp"

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "linkedin": {
      "command": "node",
      "args": ["/path/to/linkedin-mcp-server/server.js"],
      "env": {
        "LINKEDIN_ACCESS_TOKEN": "your_access_token",
        "LINKEDIN_AUTHOR_URN": "urn:li:person:your_sub_id"
      }
    }
  }
}

Google Gemini Spark

In your Gemini Spark custom applications settings, enter your public SSE endpoint URL:

https://your-server-domain.com/mcp

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Deployment

Deploy to Railway

1. Install the Railway CLI: npm i -g @railway/cli
2. Link or create a project:

   railway init
   railway up
   

3. Set environment variables:

   railway variable set LINKEDIN_ACCESS_TOKEN="your_token" LINKEDIN_AUTHOR_URN="urn:li:person:your_sub_id"
   

4. Generate a public domain:

   railway domain
   

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Development & Testing

The test suite includes 62 automated unit and integration tests covering the LinkedIn API client, all 12 tool registrations and execution handlers, and SSE / Streamable HTTP transports.

# Run tests once
npm test

# Run tests in watch mode
npm run test:watch

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License

This project is licensed under the MIT License.