linkedin-pages-mcp

MCP server for managing LinkedIn Company Pages via the official Community Management API. Post content, manage comments, track analytics, and more — all through the Model Context Protocol.

This is the first MCP server for LinkedIn Company Page management using LinkedIn's official API.

Features

16 tools total. All using LinkedIn's official REST API with proper OAuth 2.0 authentication.

Related MCP server: @isteam/linkedin-mcp

Prerequisites

1. A LinkedIn Developer Application associated with your company page

2. Community Management API product enabled (apply via the Products tab)

3. An OAuth 2.0 access token from a user who is an admin of the company page

4. Your LinkedIn Organization ID (the numeric ID from your company page URL)

Getting your Organization ID

Your company page URL looks like https://www.linkedin.com/company/111806031/ — the number is your Organization ID.

Getting an access token

LinkedIn uses 3-legged OAuth 2.0. You need these scopes:

• w_organization_social — post and comment on behalf of the company

• r_organization_social — read posts, comments, and engagement

• rw_organization_admin — manage page and read analytics

See LinkedIn OAuth documentation for the full flow.

Installation

pip install linkedin-pages-mcp

Or from source:

git clone https://github.com/MCPWorks-Technologies-Inc/linkedin-pages-mcp.git
cd linkedin-pages-mcp
pip install -e .

Configuration

Set environment variables:

export LINKEDIN_ACCESS_TOKEN="your-oauth-token"
export LINKEDIN_ORGANIZATION_ID="111806031"

Or create a .env file (see .env.example).

Usage

With Claude Code / Cursor / any MCP client (stdio)

Add to your .mcp.json:

{
  "mcpServers": {
    "linkedin-pages": {
      "command": "linkedin-pages-mcp",
      "env": {
        "LINKEDIN_ACCESS_TOKEN": "your-oauth-token",
        "LINKEDIN_ORGANIZATION_ID": "111806031"
      }
    }
  }
}

Then ask your AI assistant:

"Post an update to our LinkedIn company page about our latest release"

"Show me our LinkedIn page analytics for this month"

"Reply to the latest comments on our most recent post"

With MCPWorks (remote, token-efficient)

Add as an MCP server plugin on your MCPWorks namespace:

"Add the LinkedIn Pages MCP server to my namespace"

Then your MCPWorks functions can call LinkedIn tools from inside the sandbox:

from functions import mcp__linkedin_pages__linkedin_get_posts
from functions import mcp__linkedin_pages__linkedin_get_follower_stats

posts = mcp__linkedin_pages__linkedin_get_posts(count=5)
stats = mcp__linkedin_pages__linkedin_get_follower_stats(time_granularity="MONTH")

top_post = max(posts['elements'], key=lambda p: p.get('engagement', 0))
result = {
    'followers': stats.get('followerCount'),
    'top_post': top_post.get('commentary', '')[:100],
    'total_posts': len(posts['elements']),
}

All LinkedIn data stays in the sandbox. Only the summary returns to the AI context.

Available Tools

Posts

Comments

Reactions

Analytics

Media

Organization

LinkedIn API Limits

Development tier is granted on application. Standard tier requires a video demo review by LinkedIn (~60 days).

License

MIT License. See LICENSE.

Built by MCPWorks Technologies Inc.