LinkedIn MCP Runner

What does “LinkedIn MCP Runner” MCP Server do?

The LinkedIn MCP Runner is an official implementation of the Model Context Protocol (MCP) designed to connect AI assistants like GPT-based models with a user’s public LinkedIn data. It serves as a creative co-pilot, enabling AI tools such as Claude or ChatGPT to access your actual LinkedIn posts, analyze engagement, understand your writing tone, and help generate or rewrite posts in your unique voice. By leveraging your real content, it streamlines workflows for content creation, analytics, and engagement strategies—transforming AI assistants into LinkedIn-savvy strategists who can provide actionable insights and automate social media interaction, all while maintaining user consent and privacy.

List of Prompts

No explicit prompt templates are listed in the repository or README.

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List of Resources

No explicit MCP resources are described in the repository or README.

List of Tools

No explicit tools (such as database queries, file management, or API calls) are described in the repository or README.

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Use Cases of this MCP Server

• Personalized Content Creation
  The server enables users to generate LinkedIn posts crafted in their own voice, using insights from their previous content to maintain authenticity and maximize engagement.
• Content Analytics
  Analyze the performance of past posts to determine what resonates most with an audience, guiding future content strategies.
• Automated Rewriting
  Rewrite existing drafts or posts to better align with a user’s historic style and tone, making posts more compelling and on-brand.
• AI-Assisted Brainstorming
  Brainstorm new content ideas based on past performance data and writing patterns, ensuring relevance and creativity.
• Multi-Platform Integration
  Seamless use with both Claude and ChatGPT, allowing users to leverage LinkedIn data across their preferred AI assistants.

How to set it up

Windsurf

No setup instructions or configuration examples are provided for Windsurf.

Claude

1. Download the Claude desktop app from claude.ai/download .
2. Visit ligo.ertiqah.com/integrations/claude .
3. Click “Generate Installation Command” (authentication with LiGo required).
4. Copy the generated command and run it in your terminal.
5. Open Claude and start chatting.

No JSON configuration is shown in the documentation.

Cursor

No setup instructions or configuration examples are provided for Cursor.

Cline

No setup instructions or configuration examples are provided for Cline.

Securing API Keys

No information on API key management or environment variable usage is provided.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "MCP-name": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “MCP-name” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

Overall, the LinkedIn MCP Runner offers a unique AI-powered LinkedIn content experience, but the public documentation is missing protocol-level details—such as resources, prompt templates, and explicit tool lists. As such, developers may find it easy to use but lacking in technical transparency.

MCP Score

Rating:
Given the clear overview and use case explanations but lack of technical MCP details, I would rate the LinkedIn MCP Runner repository a 4 out of 10 for MCP clarity and developer readiness.