LinkedIn MCP Server

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

Architecture

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

Related MCP server: LinkedIn Custom MCP Server

MCP tools

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.

linkedin_get_profile

Returns the authenticated member's OpenID profile information.

linkedin_get_post

Retrieves a LinkedIn post by a urn:li:share:... or urn:li:ugcPost:... identifier.

linkedin_update_post

Updates the commentary text of an existing LinkedIn post.

Input:

{
  "post_id": "urn:li:share:1234567890",
  "text": "Updated post text"
}

The server validates the post URN and text locally, then uses LinkedIn's Posts API PARTIAL_UPDATE operation. LinkedIn enforces ownership and permission rules; permission failures are returned as structured MCP errors.

linkedin_delete_post

Deletes a LinkedIn post by its share or UGC post URN. See docs/delete-post.md for API behavior and idempotency details.

Response schema

All MCP tools use one stable response contract.

Successful calls return:

{
  "success": true,
  "data": {
    "tool_specific_field": "value"
  },
  "message": "Optional success message"
}

Failures return:

{
  "success": false,
  "error": {
    "code": "machine_readable_code",
    "message": "Human-readable description",
    "details": {}
  }
}

data contains the tool-specific payload. message and error.details are optional. See docs/response-schema.md for the complete contract and migration notes.

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

MCP tools obtain credentials through a CredentialProvider abstraction rather than reading secrets directly. The default local-development provider remains file-based for backward compatibility.

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

Production deployments can install a secret-manager-backed provider without changing any MCP tool implementation. Never commit tokens, OAuth credentials, or personal credential files. See docs/credentials.md for the provider contract, production integration pattern, and secret-handling rules.

OAuth design

The production authentication design uses LinkedIn's 3-legged authorization-code flow with the minimum scopes required by the current tools:

openid profile w_member_social

Local development uses a loopback callback, while production requires an HTTPS callback and server-side secret storage. See docs/oauth-flow.md for the flow diagram, token lifecycle, failure states, and security requirements.

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 the LinkedIn tools from the Inspector.

Testing

Run:

pytest -q

Tests use fake credentials, reusable httpx.MockTransport fixtures, sanitized LinkedIn response samples, and an automatic guard that blocks real HTTP. No LinkedIn secrets or live API access are required. See docs/testing.md for the fixture and network-isolation strategy.

Security notes

• MCP tools access secrets only through the credential-provider interface.

• Local credential files are ignored by Git and should use restrictive permissions.

• .env.example contains placeholders only.

• GitHub push protection should remain enabled.

• LinkedIn API errors are sanitized before they are returned to MCP clients.

• If a LinkedIn token is ever exposed, revoke/rotate it immediately.

For responsible vulnerability reporting, see SECURITY.md.

Contributing & collaboration

Focused bug fixes, tests, documentation improvements, MCP tool improvements, OAuth/security hardening, and API integration improvements are welcome. See CONTRIBUTING.md before starting a substantial change.

For broader backend, AI/MCP, open-source, or industrial-AI collaboration, visit Amir Behvandi's GitHub profile or join ForgeMind Discussions.

English and Persian participation are both welcome.

Portfolio description

A production-minded MCP server that exposes LinkedIn publishing and post management as structured AI tools, combining the Model Context Protocol, OAuth-based LinkedIn authentication, secure credential-provider abstraction, and REST API integration.