LinkedIn MCP Server

A lightweight Model Context Protocol (MCP) server that lets an MCP-compatible AI assistant publish public text posts to an authenticated LinkedIn profile.

Architecture

AI Assistant
     |
     | MCP / stdio
     v
LinkedIn MCP Server
     |
     | HTTPS REST API
     v
LinkedIn

MCP tool

linkedin_create_post

Publishes a public text post to the authenticated LinkedIn profile.

Input:

{
  "text": "Hello from my MCP server 🚀"
}

The tool returns the LinkedIn post ID on success.

Requirements

• Python 3.11+
• A LinkedIn developer application with the required API permissions
• A valid LinkedIn access token
• The authenticated author's LinkedIn person URN

Install dependencies:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Credentials

Credentials are intentionally kept outside Git.

Create these files in the project root:

access_token.txt
person_urn.txt

access_token.txt should contain only the LinkedIn access token.

person_urn.txt should contain a value such as:

urn:li:person:YOUR_SUBJECT_ID

Set restrictive permissions:

chmod 600 access_token.txt person_urn.txt

Never commit tokens, OAuth credentials, or personal credential files.

Run the MCP server

python -m src.linkedin_mcp.server

The server uses STDIO, so it intentionally stays running and waits for MCP JSON-RPC messages. Do not print application logs to stdout because stdout is reserved for the MCP protocol.

MCP Inspector

For local development:

npx @modelcontextprotocol/inspector \
  python -m src.linkedin_mcp.server

Then connect to the STDIO server and call linkedin_create_post from the Inspector.

Testing

Run:

pytest -q

Security notes

• Secrets are stored in local files ignored by Git.
• .env.example contains placeholders only.
• GitHub push protection should remain enabled.
• If a LinkedIn token is ever exposed, revoke/rotate it immediately.

Portfolio description

A production-minded MCP server that exposes LinkedIn publishing as a structured AI tool, combining the Model Context Protocol, OAuth-based LinkedIn authentication, secure local credential handling, and REST API integration.